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.
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:
[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)
PhDs / PostDocs position at University of Kaiserslautern
Topic: Read the following file : 2009_RA_Logistics.pdf
To apply, please email your application including cover letter, CV, research interests, copies of certificates and job references, and an electronic copy of your master thesis as soon as possible but not later than 15th October 2020to
Prof. Dr. Timo Gschwind
E-Mail: timo.gschwind@wiwi.uni-kl.de
For further information please contact Prof. Dr. Timo Gschwind.