Scholarships and Funding Opportunities

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2 PhD positions at Dalarna University (Sweden) : Deadline June 15th

Dalarna University, Faculty of Data and Information Sciences (Falun, Sweden), offers 2 PhD positions in Microdata Analysis, specialising in Artificial Intelligence and Machine Learning.

Description

The Doctoral studies in Microdata Analysis, Dalarna University, are looking to fill two PhD positions. Both are fully funded for 4 years and start as soon as possible. The positions are in the field of data sciences and artificial intelligence.

– 1st PhD position, on forecasting

The successful candidate will work on air quality data and time series forecasting using computational modelling. The objective of this thesis is to propose a method able to predict the atmospheric pollution in urban complex terrains with high spatial and temporal accuracy. Heterogonous data (meteorology, land use, traffic) will be used, in order to build models based on robust interpolation techniques (e.g. Kriging method) and advanced machine learning algorithms (e.g. Deep Neural Networks). Applicants with background in computer science, statistics, environmental engineering, or related areas are welcome. Experience in data processing and programming skills, especially in R and Python, are expected. Knowledge in QGIS presents an advantage. Completed projects/internships on topics relevant to the research area are advantageous.

You can get more information and apply for this position through this link: https://www.du.se/en/about-du/career-opportunities/vacant-positions/vacant-position/?job=1477

– 2nd PhD position, on autonomous learning

The successful candidate will work in the field of neuro evolution and autonomous learning. This paradigm expects machines to explore and observe the environment, make predictions and ultimately learn much more like their human counterparts. Genetic algorithms are used to make systems able to autonomously learn. This work involves questions about forming memory and representations, using representations to inform decision making, theory of mind, artificial consciousness, and allows for comparisons to other machine learning methods like deep or reinforcement learning. Candidates with background from biology, evolution, cognitive science, or computer science are welcome. Working knowledge of C++ and Python are required, knowledge in computer modelling and math present an advantage.

You can get more information and apply for this position through this link:

https://www.du.se/en/about-du/career-opportunities/vacant-positions/vacant-position/?job=1479

Qualifications

The General Entry Requirements for admission to the doctoral studies are as follows:

  1. A degree at master level.
  2. Completion of 240 university credits of which 60 are at advanced level, or equivalent skills acquired through other means in either Sweden or abroad.

Application procedure

Your application should be written in English and contain:

–          Cover letter and CV.

–          Copies of diploma supplement and grades.

–          Previous independent writing such as thesis.

–          Research statement (1-2 pages where you are expected to reflect on the research topic as well as your knowledge and interest in the area).

–          Two academic references.

Application deadline: 15th of June 2020.

For questions, please contact:

Prof. Yves Rybarczyk

Study director of the PhD programme in Microdata Analysis

+46 (0)23-77 83 99

yry@du.se

Märet Brunnstedt

Doctoral Studies Coordinator

mba@du.se


PhD positions in Computer Science and Artificial Intelligence at the University of Trento: Deadline June 16th

PhD positions in Computer Science and Artificial Intelligence at the Department of Information Engineering and Computer Science of University of Trento in challenging projects to develop new algorithms for planning, scheduling and acting under uncertainty, and making these operate in industrial (e.g. manufacturing, intra-logistics) and civil (e.g. hospital, pharmaceutical) relevant environments.
Are you interested in developing new algorithms, extending state-of-the-art AI techniques, and prepared to take-on real-life problems involving advanced automation and robotic applications collaborating with humans?
Join us in a team that considers the operational planning, scheduling and actuation (motion planning) of advanced robotic platforms (AGV, UAV, …) to orchestrate their operations in collaborating with human to achieve complex business relevant objectives like e.g. reduce time-span, optimize resource consumption, quickly adapt to contingencies (e.g. product and/or platform faults, changes in objectives, changes in human operations). As the capabilities (e.g. dexterity) of robotic agents grows and their adoption in solving complex tasks collaborating with humans increases together with an increase in the complexity of the objectives, the problems of finding feasible plans to orchestrate the different agents, and of dynamically adapt and react to run-time contingencies to fulfill the high level objectives are becoming more and more difficult.
The main tasks for the PhD student are to work on algorithms and artificial intelligence techniques from the fields of constraint programming, mathematical optimization, path planning, evolutionary algorithms and/or reinforcement learning i) to solve such planning, scheduling, actuation (motion planning) and reactive adaptation problems; ii) to deepen our understanding of these techniques; iii) and to show how they can be successfully combined to solve problems of industrial and civil relevance. Moreover, the PhD student will be allowed to experiment the developed techniques and algorithms in realistic-size facilities (e.g. Robotic Labs at the University, and industrial facilities joint with the University of Trento) equipped with state-of-the-art robotic and automation platforms.
Please apply through the Admission link (https://ict.unitn.it/education/admission/call-for-application) on this website: https://ict.unitn.it
Deadline for application: 16 June 2020, 4:00 Italian Time (GMT +2)
Interested candidates shall also contact Prof. Marco Roveri marco.roveri@unitn.it and to Prof. Luigi Palopoli luigi.palopoli@unitn.it.
Requirements
Applications for the PhD Program in Information and Communication Technology are accepted from applicants who hold:
  • an Italian “Laurea magistrale” instituted in conformity with Italian   Ministerial Decree 270/2004, or
  • a university degree of the previous regulations (Italian “Laurea   specialistica ” or “Diploma di Laurea”), or
  • an equivalent degree obtained abroad (Master’s degree) and   recognized as equivalent to the Italian “Laurea magistrale” by the   Admissions Committee for the sole purposes of admission to the   Doctoral programme, also within the framework of mobility and   cooperation inter-university agreements.
Applications are also accepted from students who expect to complete their degree by October 31, 2020.
Scholarship
The PhD position will be covered by scholarships.  Currently the annual gross amount of the doctoral scholarship is approximately €16.290,00. The scholarship net amount may change, depending on the country of residence and on country-specific taxation agreements.
Students who have been awarded a PhD scholarship are entitled to get a 50% increase of their scholarship when staying abroad for reasons related to their doctoral research and studies.
Each student is provided with a budget which can be used for educational and research purposes.
First year students have the priority for getting accommodation at student residences.

Postdoc at the Institute for Neural Computation, Bochum, Germany: Start in July 1st.

The Ruhr-Universität Bochum is one of the leading research universities. The
university draws its strengths from both the diversity and the proximity of scientific
and engineering disciplines on a single, coherent campus. This highly dynamic setting
enables students and researchers to work across traditional boundaries of academic
subjects and faculties.

Institute:

Institute for Neural Computation
Ruhr-University Bochum
Universitaetsstr. 150
D-44801 Bochum, Germany, EU

The Institute for Neural Computation is a central research institute at the
Ruhr-University Bochum, see https://www.ini.rub.de/. It focuses on the dynamics and
learning of perception and behavior on a functional level but is otherwise very
diverse, ranging from neurophysiology and psychophysics over computational
neuroscience to machine learning and technical applications.

Chair:

Prof. Dr. Laurenz Wiskott, see https://www.ini.rub.de/research/groups/theory_of_neural_systems/

Research topics:

We are looking for an outstanding post-doctoral researcher to work at the intersection
of model-based reinforcement learning and representation learning, e.g. for navigation
and action planing in video game environments. It is envisaged that the postdoc
builds up his/her own independent research agenda including grant proposals but
maintains close interaction with the groups by Prof. Tobias Glasmachers and
Prof. Laurenz Wiskott.

Teaching:

There is a teaching load of 4 hours per week during the semester.

Time:

The appointment will be for three years and can be extended if successful.
Anticipated starting date is July 1st.

Requirements:

Candidates should have a recent and excellent PhD in machine learning or a related
field with a focus on representation learning and/or reinforcement learning.

Salary:

Salary is 100% of salary scale TV-L E13.

Inquiries:

Informal inquiries can be addressed to Prof. Laurenz Wiskott <laurenz.wiskott@rub.de>.

Application:

Applications (CV, transcript of records for MSc and BSc, statement of purpose) should
be sent as a single pdf file to Prof. Laurenz Wiskott <laurenz.wiskott@rub.de>.

Ruhr-University Bochum is committed to equal opportunity in employment and gender
equality in its working environment. We therefore look forward to applications from
qualified women. Applications from appropriately qualified handicapped persons are
also encouraged.


PhD position is open at the University of Strasbourg (ICube) – France: starting September 2020

Self-regularised deep learning in the presence of limited data for medical imaging

The adoption of deep learning techniques in medical imaging applications has been limited by the availability of the large labelled datasets required for robust training, as well as the difficulty of explaining their decisions. This thesis will make contributions towards overcoming both of these limitations.

It will achieve this by developing approaches to learn more robust representations using explainability. These approaches will be referred to as self-regularised deep learning in the presence of limited data. The problem of domain adaptation and learning domain invariant representations in histopathological whole slide segmentation will be taken as the initial focus of this study, but this is open be expanded during the project. Current approaches fail to achieve domain invariance because of the large domain shifts between histochemical and immunohistochemistry stainings.

An initial research direction will be to develop novel training mechanisms that are aware of, and therefore avoid, situations in which the network focusses only on limited parts of the salient information (as defined by the expert through few manual annotations) will be developed. These will force a more general representation to be learnt. The benefit being threefold: the model will be more generalisable, more domain invariant, and more amenable to transfer learning.

Location: Strasbourg is a beautiful medieval city (its historic city centre is a UNESCO World Heritage Site) on the crossroads of Europe, with Germany a tram ride away, and both Switzerland and Luxembourg short train trips away. The University of Strasbourg traces its roots back to the 16th century, has numerous Nobel laureates, and is a member of several prestigious research networks and France’s Initiative d’Excellence.

ICube and SDC: Created in 2013, ICube laboratory brings together 650 researchers in the fields of engineering and computer science. The Data Science and Knowledge research team (SDC) covers a large spectrum of research in artificial intelligence, particularly data science, machine learning, and their applications. The team has close collaborations with several hospital research departments, both in Strasbourg and abroad, and through these, the research has the potential to impact medical and biological research.

Candidate Profile: The position is open to both foreign and French students who hold a Master’s degree in Computer Science. The candidate must have a good mathematical background, skills in machine learning (supervised and/or unsupervised). Experience in deep learning and representation learning would be a plus. French is not necessary, but the candidate must be confident in spoken and written English.

Send a letter of motivation, your CV, and a transcript of grades to Dr Thomas Lampert (lam1pert@uni2stra.fr – remove the numbers) and Prof Pierre Gançarski (gan1carski@uni2stra.fr – remove the numbers).

The application deadline is 20th May.

——
Dr Thomas Lampert
Industrial Chair of Data Science and Artificial Intelligence
Tel: ‭+33 3 68 85 44 44‬

PhD position – Deep Reinforcement Learning Agents Revealing Uncertainties in Blockchain Systems: Start in September 2020

A PhD position is open at the CEA LIST (Paris area) on  “Deep Reinforcement Learning Agents Revealing Uncertainties in Blockchain Systems” which is planned to start in September 2020.

Please find below the announce and the complete PhD subject:

[1] Announce. https://academicpositions.fr/ad/cea-tech/2019/phd-position-deep-reinforcement-learning-agents-revealing-uncertainties-in-blockchain-systems/138660

[2] Detailed description. https://www.instituts-carnot.eu/sites/default/files/images/Emploi_CEALIST_Thèse_OG_v2.pdf

Best regards,

Önder GÜRCAN

Laboratory of Trustworthy, Smart & Self-Organizing Information Systems (LICIA)

CEA LIST


PhD position at University of Strasbourg- France for September 2020

A PhD position is open at University of Strasbourg (ICube lab) – France for September 2020

Interactive collaborative constrained clustering for remote sensing time series analysis

Topic: Analyzing time series of remote sensing images using supervised methods requires that the classes sought be perfectly known and defined and that the expert be able to provide a learning data set that is sufficient in number and quality. Facing the difficulty of obtaining sufficient examples to efficient remote sensing time series analysis, we propose to develop an interactive method of collaborative clustering under constraints. The idea is to allow the expert to add “on the fly” constraints to guide the clustering process in order to produce clusters closer to the expert’s “intuitions” i.e. potential thematic classes. To do so, the expert will be helped by advice or proposals for new constraints issued by the method itself.

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 remote sensing image analysis is welcome.

Send your CV, transcript of grades and motivation to Pierre Gançarski (gancarski@unistra.fr) and Thomas Lampert (lampert@unistra.fr)


PhD position in Femto-ST / IRIT, France, in task scheduling with uncertainties

A PhD position is available in the ANR DataZero2 project (http://datazero.org). The PhD work will start in september and will take place between Besançon (Femto-ST) and Toulouse (IRIT) in France. The PhD student will tackle issues linked to scheduling tasks with uncertainties in a data centre powered by 100% renewable energy.

To apply please send a CV, a motivation letter and your academic transcript.

If you have any question do not hesitate to contact us.

Contact:
Jean-Marc PIERSON: jean-marc.pierson@irit.fr
Laurent PHILIPPE: laurent.philippe@univ-fcomte.fr


Laurent PHILIPPE
Directeur du mésocentre de calcul de Franche-Comté

Institut FEMTO-ST, Dep. DISC
UFR ST – 16, route de Gray
25030 Besançon Cedex, FRANCE

laurent.philippe@univ-fcomte.fr
http://members.femto-st.fr/Laurent-Philippe
tel/fax: +33 (0) 3 81 66 66 54 / 64 50

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