PhD positions: AIDD H2020-MSCA-ITN-2020 project: development of interpretable deep neural networks models for drug discovery
Machine learning is changing our society, as exemplified by speech and image recognition applications. Also, the life sciences change rapidly through the use of artificial intelligence, and it is expected that fields like drug development can take advantage of machine learning. The main goal of the AIDD project is to train and prepare the next generation of scientists who need to have skills in both machine learning and drug discovery and will, after graduating, be able to contribute to speed up the drug development process. The European Marie Skłodowska-Curie Innovative Training Network funds the AIDD project that brings together twelve academic partners (Helmholtz Zentrum München (coordinator), Germany; Aalto University, Finland; Freie Universität Berlin, Germany; Katholieke Universiteit Leuven, Belgium; Johannes Kepler Universität Linz, Austria; The Swiss AI Lab IDSIA, Switzerland; TU Dortmund, Germany; Universiteit Leiden, Netherlands; Université du Luxembourg, Luxembourg; University of Vienna, Austria; Universitat Pompeu Fabra, Spain and Vancouver Prostate Center, University of British Columbia, Canada) as well as four industrial partners (AstraZeneca, Sweden; Bayer Aktiengesellschaft, Germany; Janssen Pharmaceutica NV , Belgium and Enamine Limited Liability Company, Ukraine).
The AIDD network offers 15 PhD fellowships. The employed fellows will be supervised by academics who have the solid technical expertise and have contributed to some of the fundamental AI algorithms which are used billions of times each day in the world, and by machine learning scientists working at pharmaceutical companies. The developed methods by the fellows will contribute to an integrated "One Chemistry" model that can predict outcomes ranging from different properties to molecule generation and synthesis. The network will offer comprehensive, structured training through a well-elaborated Curriculum, online courses, and six schools.
Each fellow will perform research for 1.5 years at an academic partner and 1.5 years at an industrial partner.
For more information, visit http://ai-dd.eu
Essential Skills and Experience
- Master's degree in computer science, cheminformatics, bioinformatics or equivalent subject;
- Courses in machine learning;
- Courses in programming.
- Experience in software engineering;
- Proven experience of Python programming;
- Experience of deep learning libraries for instance TensorFlow or PyTorch);
- Experience with libraries such as RDKit or scikit-learn would be of advantage;
- Good proficiency in modern software development tools, such as git;
- Courses in drug development.
The ideal candidate will also demonstrate enthusiasm for propelling scientific questions with a positive and problem-solving attitude and the willingness to undertake complex analysis tasks in a timely fashion. Excellent English is required, both spoken and written, and the ability to work effectively both separately and in cross-functional teams. We also believe that you enjoy teamwork, have a collaborative nature, and will be an encouraging colleague to all.
Early-Stage Researchers (ESRs) shall, at the time of recruitment by the host organization, be in the first four years (full-time equivalent research experience) of their research careers and have not been awarded a doctoral degree.
At the time of recruitment by the host organization, researchers must not have resided or carried out their main activity (work, studies, etc.) in the country of their host organization for more than 12 months in the 3 years immediately prior to the reference date. Compulsory national service and/or short stays such as holidays are not taken into account. As far as international European interest organizations or international organizations are concerned, this rule does not apply to the hosting of eligible researchers. However, the appointed researcher must not have spent more than 12 months in the 3 years immediately prior to their recruitment at the host organization.
Eligibility and Mobility Rules are defined only at the first employment.