PhD studentships in Epistemic Artificial Intelligence @ Oxford Brookes
The Faculty of Technology, Design and Environment at Oxford Brookes University is pleased to offer 2 three-year full-time PhD studentships to students commencing September 2021, funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 964505 “Epistemic AI”.
The successful candidates will join the Visual Artificial Intelligence Laboratory under the supervision of Professor Fabio Cuzzolin.
Project description
The Visual Artificial Intelligence Laboratory is a fast-growing research unit currently running on a budget of £3 million from eight live projects funded by the EU (2), Innovate UK (2), the Leverhulme Trust and others. Our research interests span artificial intelligence, uncertainty theory, machine learning, computer vision, autonomous driving, surgical and mobile robotics, AI for healthcare. The Lab is currently pioneering frontier topics in AI such as machine theory of mind, self-supervised learning, continual learning and future event prediction.
The PhD students will join the Lab’s work towards a new Horizon 2020 FET (Future Emerging Technologies) project “Epistemic AI” coordinated by Prof Cuzzolin and whose other partners are TU Delft (Netherlands) and KU Leuven (Belgium). The project started in March 2021 and has a duration of 4 years.
The project’s overarching objective is to develop a new paradigm for a next-generation artificial intelligence providing worst-case guarantees on its predictions thanks to a proper modelling of real-world uncertainties. The project re-imagines AI from the foundations, with the aim of providing a proper treatment of the ‘epistemic’ uncertainty stemming from a machine’s forcibly partial knowledge of the world by means of advanced uncertainty theory. All new algorithms and learning paradigms are to be tested in the context of autonomous driving.
Requirements
We seek a highly competent candidate to submit their thesis within 3 years. Candidates should have a strong mathematical background, specifically in optimisation, probability and statistics, and a good first degree in Machine Learning, Artificial Intelligence or related fields. Applicants are also expected to have Research experience in Machine Learning or Artificial Intelligence, and good coding skills in Python and/or C++. Knowledge of uncertainty theory, including belief functions, random sets or imprecise probabilities is desirable, as is experience of coding in Torch, PyTorch, Tensorflow or Caffe, and experience of work in autonomous driving
How to apply
To apply, please request an application pack by emailing tdestudentships@brookes.ac.uk, quoting “PhD Studentships in Epistemic Artificial Intelligence“.
Fully completed applications must be sent to tdestudentships@brookes.ac.uk, by 20 June 2021. As part of the application process you must submit your CV, along with a supporting statement (2-page maximum) which explains why you believe you are the best candidate for this studentship.
Please be advised that the selection process may involve an interview.
For informal requests contact Prof Fabio Cuzzolin (fabio.cuzzolin@brookes.ac.uk).
Eligibility: Home / EU / International Students
Bursary: £16,540 per year
Fees: Tuition fees will be paid by the university for 3 years
Deadline for applying: 20 June 2021
Start date: September 2021
PhD Student Position in Robot Learning at Aalto University, Finland
The Aalto Robot Learning research group operates in the intersection of artificial intelligence and robotics. In particular, we focus on reinforcement learning, robotic manipulation, decision making under partial observability, imitation learning, and decision making in multi-agent systems. The goal of the research group is to help robots understand what they need to learn in order to perform their assigned tasks, and, thus, make robots capable of operating on their own and pro-actively help humans. To accomplish these goals the research group develops novel decision making methods and uses these methods to solve unsolved robotic tasks. For more information, please see http://rl.aalto.fi
The Robot Learning research group is seeking a talented PhD student with strong interest in Robot Learning and
* Mobile manipulation and
* Reinforcement learning
The main task of the doctoral candidate will be to develop new state-of-the-art reinforcement learning (RL) methods and utilize them on a real robot. The developed methods allow robots to operate in unstructured partially observable real-world environments found in household robotics, construction, work sites of mobile heavy machines, agile manufacturing, elderly care, handling dangerous materials, or even disaster scenarios such as Fukujima.
The research focus will be on developing reinforcement learning method techniques related to partial observability, memory representations, exploration, Bayesian reinforcement learning, curriculum reinforcement learning, and Monte Carlo tree search. The doctoral candidate will apply the methods on real robots such as the Boston Dynamics Spot robot with a Spot arm. The exact direction of the research is chosen depending on your experience and interests.
Please relate clearly to (some of) these research topics in your Letter of Motivation.
Outstanding students and researchers from the areas of Machine Learning, AI, and Robotics and related areas including Reinforcement Learning, Control Engineering, Computer Vision, Statistics & Optimization, or Mathematics & Physics are welcome to apply. The candidate is expected to conduct independent research and at the same time contribute to the topics listed above. Successful candidates can furthermore be given the opportunity to work with undergraduate, M.Sc. and PhD students.
The research group collaborates with research groups in computer vision, robotics, construction, mobile heavy machines, human-robot interaction, reinforcement learning, imitation learning, multi-agent systems, under-water vehicles, and robot motion planning. Moreover, two of the PhD students in the research group are located at the Intelligent Autonomous Systems institute at TU Darmstadt, Germany so there will be ample opportunities for international collaboration.
WE OFFER
The position will be filled for a period of 1+3 years. The starting date is in September 2021 or as mutually agreed. The salary will be based on both the job requirements and the employee’s personal performance in accordance with the salary system of Finnish universities. A typical starting salary for a doctoral candidate is approximately 2500 EUR/month. The salary increases with experience.
We offer a wide range of staff benefits, such as occupational health care, flexible working hours, excellent sports facilities on campus and several restaurants and cafés on campus with staff discounts.
HOW TO APPLY
Please submit your application through our online recruitment system. Click “Apply now!” at the end of the web page
https://www.aalto.fi/en/open-positions/phd-student-position-in-robot-learning
The closing date for applications is June 24, 2021 (23:59 EEST (GMT+3)). Please write your application and all the accompanying documentation in English and attach them in PDF format.
Please attach only the following documents to your application:
* A letter of motivation and description of your research interests (max. 1 page)
* Curriculum vitae (including the contact details of two referees).
* A list of publications (if any) as a part of the curriculum vitae
* PDF copy of your MSc and BSc degree certificates, including transcripts of all university records and their English translations (Finnish and Swedish certificates are also accepted)
ADDITIONAL INFORMATION
For further information, please contact Assistant Professor Joni Pajarinen, joni.pajarinen@aalto.fi. Additional information in recruitment process related questions, please contact HR Coordinator Jaana Hänninen, jaana.hanninen@aalto.fi.
ABOUT AALTO UNIVERSITY, HELSINKI, AND FINLAND
Aalto University (aalto.fi) is located in Finland. Finland is among the best countries in the world according to many quality of life indicators, including being the happiest country in the world (UN study 2018 and The World Happiness Report 2019) and the safest country in the world (World Economic Forum report 2017). Aalto University is the foremost university in Finland in Engineering, Design and Business. Less than a 15 minutes metro ride away from center of Helsinki, capital of Finland, Aalto offers access to rich cultural and social life to help maintain healthy work-life balance.
Aalto University is a community of bold thinkers where science and art meet technology and business. We are committed to identifying and solving grand societal challenges and building an innovative future. Aalto has six schools with nearly 11 000 students and a staff of more than 4000, of which 400 are professors. Our main campus is located in the Helsinki metropolitan area, Finland. Diversity is part of who we are, and we actively work to ensure our community’s diversity and inclusiveness in the future as well. This is why we warmly encourage qualified candidates from all backgrounds to join our community.
PhD Position on data-driven Energy Efficiency 5G network modeling and optimization
The network optimization research team of the Advanced Wireless Technology Lab at the Huawei France Research Center, located in the Paris area, together with the Laboratoire de Traitement et Communication de l’Information (LTCI) in Telecom Paris, is proposing a PhD thesis on data-driven Energy Efficiency 5G network modeling and optimization.
We are looking for a student with a strong background on wireless networks and expertise in applied mathematics, statistics, and machine learning.
Potential candidates should send before the 23/06/2021 a resume and a motivation letter, together with two references, to the following mail addresses: antonio.de.domenico@huawei.com; marceau.coupechoux@telecom-paris.fr
Kind Regards,
Antonio De Domenico
PhD thesis, Lille Nord Europe , INOCS and Spirals team
Design of incentives for “Green” Cloud computing
Candidates should send a CV and a motivation letter to Luce Brotcorne(Luce.Brotcorne@inria.fr) by June 24, 2021.
Objective.
This project focuses on the study of the environmental impacts of the increasingly preponderant use of digital technology, and more particularly its impact on the resulting energy consumption.
The objective of this project is not only to make all stakeholders in this value chain (operators, service providers and the general public) aware of the ecological impact of digital technology, but also to develop a set of best practices for the implementation of more environmentally friendly digital services.
More specifically, our objective is to identify incentives, such as new tariffs, that can change customer behavior so that they contribute to reducing the energy impact while maintaining a good quality of service.
General Context
Until now, cloud computing operators, have applied pricing strategies driven by the reservation of virtualized resources.
In particular, in the context of an Infrastructure-as-a-Service (IaaS) offering, customers order computing resources in the form of virtual machines with specific characteristics (CPU, RAM, storage, network throughput, etc..
Customers must choose between guaranteed performance and a reduction in operating costs (and indirectly in energy consumption).
The research activity that we wish to engage aims at identifying a viable balance between these 2 options to allow customers to benefit from guaranteed performance while minimizing the energy footprint of the infrastructure
In particular, by considering a guaranteed performance offer, we wish to study the possibility of offering discounts to customers to encourage them to free up resources when they have no use for them, thus allowing us to offer these available resources to other customers—or services—while smoothing out overall demand.
Through this new offer, and its dynamic pricing, we wish to maintain a high performance criterion while chasing the waste of underused resources.
This estimation of underutilized resources requires in particular the realization of an energy balance of a hosted application in order to be able to reason about the energy needs of a customer and the observable margins of energy consumption.
If the margins of consumption are stable and important, it can be considered to recommend a virtual server profile more adapted to the needs of the hosted application.
If these margins are more unstable and variable over time, temporary discounts can be considered and submitted to the customer.
Other pricing options can take into account overall demand and offer discounts during periods of high demand for customers who are willing to give up resources to other customers and services under stress.
In this framework, integrating customer behavior into the optimization process is very important (bi-level optimization problem with hierarchical structure).
Research Environnement
The Phd thesis will be co-supervised by
– Romain Rouvoy from the Spirals Team https://team.inria.fr/spirals/
– Luce Brotcorne and Bernard Fortz from the INRIA INOCS team (http://team.inria.fr/inocs)
Skills
Combinatorial Optimization, Mathematical Programming.
Operating system virtualization technique
Coding in C++, Python.
Other knowledge appreciated: Optimization software.
PhD opening in Human-Robot Teaming at University of Texas at San Antonio
The Unmanned Systems Lab at University of Texas at San Antonio has 2 PhD openings in the area of human-robot teaming. The main research goal is to develop new learning, control, and optimization algorithms to enable low-barrier human-robot collaboration in complex environments. We seek to understand the fundamental limitations and principles that humans and robots should work together for effective and efficient teaming. These challenges include, but not are limited to, human behavior learning, imitation learning, goal reasoning, multi-agent reinforcement learning, and role assignment in team environments.
Position description:
Required
– A Bachelor’s degree in electrical engineering, computer science, computer engineering, or a related field;
– Strong background in mathematics, statistics, and machine learning;
– Excellent writing and communication skills;
– Proficiency in Matlab, C++, or Python.
Preferred
– Master’s degree
– Experience on Robot Operating System (ROS), reinforcement learning, and computer vision
– Hands-on experience on robotics (hardware or software)
– Demonstrated research experience (i.e., projects or publications)
How to apply:
Send the following documents in a single PDF file
– One-page cover letter describing your interest, goal, and how your background fits well;
– CV or resume
– Transcripts
to yongcan.cao@utsa.edu.
PhD position – autopilot testing with RL
Airbus and the Supaero RL Initiative are offering a 3-years funded position on autopilot testing using RL, starting in fall 2021. Project goals and contact details are in the attached document.
Best,
Emmanuel
—
*Emmanuel Rachelson* <http://people.isae-supaero.fr/emmanuel-rachelson>
RL research at ISAE-SUPAERO <https://SuReLI.github.io>
Data and Decision Sc. training <https://supaerodatascience.github.io>
Tout message après cette ligne a été ajouté sans mon consentement.
Any message after this line has been added without my consent.
Postdoc Position in Deep RL/Ad Hoc Teamwork, University of Edinburgh
Applications are invited for a full-time *Postdoctoral Research Associate* position in the Autonomous Agents Research Group (https://agents.inf.ed.ac.uk) led by Dr. Stefano V. Albrecht in the School of Informatics, University of Edinburgh.
The successful candidate will join the 3-year project “Explainable Reasoning, Learning and Ad Hoc Multi-agent Collaboration” funded by the US Office of Naval Research (ONR), with project partners at the University of Texas at Austin and the University of Birmingham.
This particular position will focus on the development of deep reinforcement learning algorithms for ad hoc teamwork in open multi-agent environments. For more details about the problem and research scope, see our recent ICML 2021 paper (https://arxiv.org/abs/2006.10412).
The post is available immediately for up to 3 years.
For more information about the position, requirements, and how to apply (deadline 7 July 2021) see: https://elxw.fa.em3.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/job/1222/?utm_medium=jobshare
Informal enquiries can be sent to Stefano Albrecht (s.albrecht@ed.ac.uk).
—
Dr. Stefano V. Albrecht
Assistant Professor, School of Informatics, University of Edinburgh
Head of Autonomous Agents Research Group (http://agents.inf.ed.ac.uk)
Royal Society Industry Fellow, Five AI (https://www.five.ai)
Twitter: @UoE_Agents
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
Post Doctoral position – ROC group
Contact : MJ. Huguet (huguet@laas.fr)
Postdoc in Optimization Toulouse (France)
Certification and validation of computational results is a major issue for modern sciences raising challenging problems at the interplay of mathematics and computational aspects of computer science. One can emphasize in this context several applications arising in energy networks, such as alternative current optimal power flow (AC-OPF), with a crucial need of certification. OPF problems can be modeled with polynomial optimization, which consists in computing the infimum of a polynomial function under (in)equality constraints. The emergence of polynomial optimization as an exciting new field goes back to the last two decades and has led to striking developments from across fertilization between (real) algebraic geometry, applied mathematics, theoretical computer science and engineering. The backbone of this powerful methodology is the “moment-sums of squares” (moment-SOS) hierarchy, relying on semidefinite programming (SDP). Several convex relaxations have been recently provided with the goal of solving AC-OPF instances to global optimality. These efforts led to efficient solution algorithms that can solve many instances found in the literature, which model real-world networks. The concurrent methods usually perform costly domain partitioning and spatial branching on continuous variables. Our framework shall overcome these issues by providing fast yet accurate bounds.
Requirements: A successful candidate will have a strong background in applied mathematics or physics, excellent programming skills, as well as a working knowledge of convex optimization. The candidate should be highly motivated and creative.
Funding: This postdoc is part of the FASTOPF project, funded by AMIES (Agence Math Entreprise). The Postdoc candidate will be hosted at LAAS, with a co-supervision together with RTE.
Contacts : Victor Magron vmagron@laas.fr, Jean-Bernard Lasserre lasserre@laas.fr, Jean Maeght jean.maeght@rte-france.com -- Victor Magron
PhD proposal Université du Havre
The RI2C team (interaction networks and collective intelligence) of the LITIS lab launches a call for a computer sciences thesis (Operations Research), starting from september 2021 at Le Havre Normandy University, entitled:
“Interconnection beween a point to point transportation system and a public transport network”
Applicants should have a master’s degree in computer science or applied mathematics
(or an equivalent degree) and have a strong background in Operations Research,
Combinatorial Optimization and Mathematical Programming. Knowledge in the application
domain of transportation, in metaheuristics or in robust optimization would be a plus.
The thesis will be supervised by Christophe Duhamel and Eric Sanlaville.
Summary of the thesis topic:
Classical urban public transportation systems are designed to cover a massive demand in
urban mobility. They are based on high-capacity vehicles, predefined routes and fixed
schedules. This kind of service is generally well suited to urban centers and business
hours. However, it becomes less suitable (i) when connecting suburban, industrial and
even rural areas, (ii) when it is necessary to cover night-time travels, or (iii) when
people have mobility restrictions.
In this case, on-demand transport systems are more interesting because they provide
transport according to individual needs, usually by means of smaller vehicles. Both types
of service are now offered by transit system operators, but often within fixed perimeters:
it is up to the user to plan the right sequence of services in order to complete the trip.
We propose to study an alternative in which the operator offers on demand transport,
including the possibility of dropping people off at the stops of an urban transport network.
The objective is to benefit from the existing network and to provide the flexibility
required by the lower frequency of suburban transport requests while ensuring a correct
level of Quality of Service for users. Thus, one transport request can be met completely or
partially by the flexible network. In the latter case, part of the trip will be made on
the conventional network and the trip will take into account multimodal constraints.
The optimization problems involved in this project will be addressed through heuristics,
and then metaheuristics. We will also study the robustness of the obtained solutions and
the best online strategies for such kind of integrated service.
Applications (CV, cover letter, transcripts) should be sent by email to both supervisors
before June 14th. You are strongly encouraged to contact the supervisors directly.
christophe.duhamel@univ-lehavre.fr
eric.sanlaville@univ-lehavre.fr
-- Eric Sanlaville
Postdoc position in Strasbourg (2021): DL, Domain Adaptation, Multi-Modal Representations
A Postdoc position is open at University of Strasbourg (ICube lab) – France to start before November 2021
Deep Learning, Domain Adaptation, Multi-Modal Representations
The position will be funded for two years (initially for one year, renewable for an additional year). The candidate will join the SDC research team under the supervision of Dr Thomas Lampert, the Chair of Data Science and Artificial Intelligence, and join his international team of PhD students and engineer to develop novel deep learning approaches to domain invariant representation learning (particularly in multi-modal data), with application (but not restricted) to Medical Imaging and Remote Sensing. The funding is not connected to a particular project, so it is the perfect opportunity for a strong candidate to explore new directions under the supervision of the Chair.
The successful candidate will have (or will soon obtain) a PhD in computer science or related domain and have experience in deep learning and applied machine learning and a strong level of written and spoken English. Experience with transformers, GANs, autoencoders, and/or unsupervised/self-supervised DL (autoencoders, etc) would be a plus. You will join a growing team and will have the freedom to follow your interests in a direction complementary to the abovementioned research focusses. You will be expected to target leading outlets in the field of machine learning and a strong track record in CVPR/ICCV/ECCV, NIPS/ICML/ICLR, or PAMI/IJCV/TIP. Candidates who are able to carry out the highest quality research independently, to co-supervise PhD students, and to give their input on a number of projects being carried out in the team are pursued. You will have access to state-of-the-art hardware for deep learning.
Send a letter of motivation, your CV, and an example publication to Dr Thomas Lampert (lam1pert@uni2stra.fr – !remove the numbers!).
The position will remain open until a suitable candidate is found and the starting date will be agreed upon with the successful candidate but will be no later than 1st November 2021.
Detailed Description: https://seafile.unistra.fr/f/8c723d6a74834196b1aa/?dl=1
PhD Positions Joint Doctoral Program NITech Japan
Postdoc positions in systems biology and applied mathematics
We would appreciate that you share the job offers with your contacts.Sincerely,
Clémence Frioux, PhD
Junior researcher
cfrioux.github.io
Ph.D. in LIMOS (France) laboratory with co-supervision in Italy
Thank you for disseminating to your students.Subject: Resolution of inventory and transportation management problems using Artificial Intelligence.
Location: LIMOS laboratory – Clermont-Ferrand, France
Supervisor: Philippe Lacomme (LIMOS)
Start date (desired): as soon as possible (before December 2021)
Application deadline: 1 September 2021Contacts : Philippe Lacomme (placomme@isima.fr) ; Katyanne Farias (katyanne.farias_de_araujo@sigma-clermont.fr) ; Manuel Iori (manuel.iori@unimore.it)
Best regards,
Maître de Conférences
SIGMA – LIMOS
Campus de Clermont-Ferrand / Les Cézeaux, CS 20265
63175 Aubière Cedex, France
3-year PhD Position in Toulouse in Control of Unconventional Aerial Vehicles (via classical and learning based methods)
Open PhD Position at ENAC
We seek applications for a three-year PhD student position in control of unconventional drone configurations.
The PhD candidate will be working within an established group of researchers and engineers while having hands-on experience on the software and hardware design, manufacturing, ground and flight testing of invitational flying vehicles.
Main objective of the work is to develop control algorithms that can learn both low level attitude stabilization and also high level guidance and navigation of various different aerial vehicle configurations. Ideally, the developed controller will be able to reconfigure itself in case of possible faulty behavior of its actuators (when applicable).
A detailed description of the thesis can be found in this link <https://drive.google.com/file/d/1VHC23nqSoVZKBQUB9cAJp6uzF6KIVHLv/view?usp=sharing> (or here : https://drive.google.com/file/d/1VHC23nqSoVZKBQUB9cAJp6uzF6KIVHLv/view?usp=sharing )
Interested applicants should contact Murat Bronz by e-mail at murat.bronz@enac.fr <mailto:murat.bronz@enac.fr> including the items below at latest on 14th of June 2021 (The earlier the better!).
– a motivation letter
– a CV
– a list of previous publications (if applicable)
– a copy of relevant grade documents
– full contact details of the candidate
2 Postdoc offers from CEA Grenoble
Here are 2 postdoc offers from CEA Grenoble dealing with:
- Adversarial examples PostDoc2021_Adversarial
- Incremental learning PostDoc2021_Incremental
Do not hesitate to transfer or contact the responsible for more info (contact in the attached documents).
Best,
—
Marielle Malfante,
PhD, Ingénieur Chercheur Intelligence Artificielle.
CEA Grenoble – bâtiment 50C – bureau C420.
Tel: 04 38 78 97 61.
DRT/LIST/DSCIN/LIIM.
PhD position at LIRIS/INSA-Lyon on Deep Learning for Robotics
Seed Grant for New African Principal Investigators
We are pleased to announce a new TWAS Research Grant Programme, through the support of the German Federal Ministry of Education and Research (BMBF), Seed Grant for New African Principal Investigators (SG-NAPI). The call for 2021 is now open and will remain open until 27 July 2021.
The programme is specifically aimed at PhD graduates from selected African countries, who obtained their PhD abroad within the last 5 years and returned home within the last 36 months or will return home before the end of 2021.
Attached is a flyer for the programme that can be circulated electronically or printed and put up at university notice boards. We appreciate your help with the dissemination of this new programme.
Full details about the SG-NAPI Programme are available on the TWAS website
For any queries, kindly write to sgnapi@twas.org
Thanking you.
Kind regards,
TWAS Science Diplomacy Programme
Ph.D. – Explainable Artificial Intelligence and therapeutic target identification
Oncodesign SA and the Distributed Artificial Intelligence and Knowledge laboratory (CIAD LAB – UMR 7533) at the University of Bourgogne Franche-Comte have a vacancy for a Ph.D. fellowship.
About the position
————–
Biological networks are very effective tools for modeling, analyzing, and discovering new biological interactions in complex biological systems.
In recent years, network models and algorithms have been used for the development of precision medicine for many diseases.
The mathematical machinery at the heart of this research field is based on graph theory, a widely studied disciplinary field.
This is also associated with machine learning on structured data in the form of graphs.
A big challenge is to create better modeling tools to integrate human expertise and artificial intelligence techniques to exploit big data for clinical research and drug development, to advance a better understanding of health and disease and formulate a hypothesis on a new mechanism of actions and identify corresponding innovative therapeutic targets. . To meet this challenge, many emerging works propose the design of explainable AIs, allowing the identification of innovative therapeutic targets. These explainable AIs combine connectionist AI approaches such as deep learning, neural networks, etc., and causal AIs based on modeling causal graphs of knowledge derived from the knowledge of domain experts.
This research will address questions such as:
– How to aggregate data sources from heterogeneous biological and medical databases while maintaining the consistency of the associated knowledge?
– What are the best functions for analyzing raw data to extract knowledge?
– How to develop in silico prediction of new therapeutic targets which will then be validated in vitro and/or in vivo?
Required selection criteria
————–
The qualification requirement is that you have completed a master’s degree with a strong academic background in one or more of biology, computer science, and engineering, mathematics, or equivalent education with a grade of the first third of the promotion.
The candidate must have a background in computer science with ideally skills in machine learning and/or knowledge engineering.
Knowledge in the field of biology will be required.
Applicants must provide evidence of good English language skills, written and spoken. Mastery of the French language will be appreciated.
Preferred selection criteria
– Background in Artificial Intelligence and/or Data Mining/Data Science applied in medicine and biology
– A candidate with some industrial experience in the aforementioned areas will get preference.
– Publication activities in the aforementioned disciplines will be considered an advantage.
Salary and conditions
————–
Ph.D. candidates are remunerated by the company. The amount of the salary can be negotiated with the company.
The appointment is for a term of 3 years and can be extended beyond the Ph.D. defense.
Appointment to a Ph.D. position requires that you are admitted to the Ph.D. program in computer science and that you participate in an organized Ph.D. program during the employment period.
About the application
————–
This research is funded by the French government and the Oncodesign SA (https://www.oncodesign.com/en/) in the frame of CIFRE Ph.D. (Conventions Industrielle de Formation par la Recherche).
Oncodesign and the CIAD laboratory have initiated a scientific collaboration in the field of precision medicine.
This collaboration concerns the identification of new therapeutic targets and the acceleration of the research and development phases of new molecules.
The job will be located at Dijon, a gastronomic and touristic French city at 1.5 hours from Paris by train.
The CIAD Lab and Oncodesign are 500 meters distance.
Application deadline: 30.06.2021
————–
If you have any questions about the position, please contact: Christophe Nicolle email: cnicolle@u-bourgogne.fr
Maître de Conférences – CNU 27
PhD in Algorithms for Star Discrepancy Problems
We are looking for a PhD candidate to do research on efficient
algorithms for star discrepancy problems at LIP6, Sorbonne Université,
Paris, France. Extended research visits to the University of Coimbra,
Portugal, are also possible.
Star discrepancy measures how regularly a set of points is distributed
in a given space. Point sets of low star discrepancy have several
important applications including Quasi-Monte Carlo integration,
financial mathematics, optimization, design of experiments, and many
more. The main goal of this PhD project is the design and the analysis
of efficient algorithms to address the discrepancy subset selection
problem, that is, to find a subset of a point set that minimizes star
discrepancy.
The PhD student will be supervised by Carola Doerr from LIP6, Sorbonne
Université and Luís Paquete from the University of Coimbra. Applicants
are expected to have excellent skills on design and analysis of
algorithms.
The earliest starting date is October 2021.
Firm application deadline is May 16.
Candidates are strongly advised to get in touch with us before submitting their application.
More details about the PhD project are available at
http://www-ia.lip6.fr/~doerr/2021-PhD-Discrepancy.pdf
Questions about the position should be sent to Carola.Doerr at lip6.fr
or/and paquete at dei.uc.pt.
With best wishes,
Carola Doerr, CNRS, LIP6, Sorbonne Université
—
Carola Doerr, CNRS researcher at LIP6, Sorbonne University, http://www-ia.lip6.fr/~doerr/
PhD position on multi-agent reinforcement learning
A fully funded PhD position is available starting Spring 2022 on multi-agent reinforcement learning, human-robot teaming, and multi-objective planning. Research will be conducted in the Unmanned Systems Lab, Department of Electrical and Computer Engineering at The University of Texas at San Antonio (UTSA), under the supervision of Dr. Yongcan Cao.
Position description:
Required
– A Bachelor’s degree in electrical engineering, computer science, computer engineering, or a related field;
– Strong background in mathematics, statistics, and machine learning;
– Excellent writing and communication skills;
– Proficiency in Matlab, C++, or Python.
Preferred
– Master’s degree
– Experience on Robot Operating System (ROS), reinforcement learning, and computer vision
– Hands-on experience on robotics (hardware or software)
– Demonstrated research experience (i.e., projects or publications)
How to apply:
Send the following documents in a single PDF file
– One page cover letter describing your interest, goal, and how your background fits well;
– CV or resume
– Transcripts
to yongcan.cao@utsa.edu.
PhD position in Strasbourg (Sept. 2021)
A PhD position is open at University of Strasbourg (ICube lab) – France for October 2021
Title: Multiparadigm interactive collaborative learning of image time series in interaction with the medical expert
Topic: Analysing heterogeneous image time series using supervised methods requires that the classes sought are perfectly known and defined and that the expert is able to provide a sufficient learning data set both in number and quality. Unfortunately, unsupervised methods can lead to results which do not match the potential objects/classes of interest. Faced with these difficulties, we propose to develop an innovative a collaborative framework of interactive multi-paradigm collaborative learning in which different methods, supervised or not, work together to produce “better” result. The objective of the thesis is two-fold. First, it will be to answer scientific questions regarding collaborative process such as what information to share (data, hypotheses, constraints…), how to evaluate results, what are good coordination mechanism, how to combine the opinions of the different agents and how to ensure convergence. Second, it is to enable the expert to add “on the fly” information (labels, constraints, etc.) used to guide the learning process in order to produce clusters and models closer to the expert’s “intuitions”. To do this, the expert will be actively assisted by the system, which will for example offer advice or proposals for new constraints or labelling of objects
Different fields in health domain of application are envisaged to validate our proposition. They will mainly concern situations in which the types of evolution are both numerous and not very formalized as for instance, images time series.
To apply:
The positions are offered to both foreign and French students who hold a Master degree in computer science. The candidate must have good skills in data analysis and more particularly in supervised or unsupervised classification of time series. Skills in image analysis is welcome.
Good knowledge of English (French is not mandatory)
As required by the Doctoral School of the University of Strasbourg, the candidate must have obtained all his/her Master’s (and Bachelor’s) semesters or equivalent with a grade above 12/20. He/she must also be ranked among the top 20% of graduates of his/her Master promotion.
Send your CV, transcript of grades, ranking and motivation to Pierre Gançarski (gancarski@unistra.fr) and Antoine Cornuéjols (antoine.cornuejols@agroparistech.fr)
Detailed Description: https://seafile.unistra.fr/f/3c4f54836ab44eec99b5/https://seafile.unistra.fr/f/3c4f54836ab44eec99b5/https://seafile.unistra.fr/f/3c4f54836ab44eec99b5/https://seafile.unistra.fr/f/3c4f54836ab44eec99b5/https://seafile.unistra.fr/f/3c4f54836ab44eec99b5/