PhD Position in Machine Learning within Marie Skłodowska-Curie ITN "MLFPM”

Date limit
Fundación Progreso y Salud (FPS)


  • RESEARCH FIELD: Biological sciences, Computer science, Engineering, Mathematics, Medical sciences, Technology
  • RESEARCHER PROFILE: First Stage Researcher (R1)
  • APPLICATION DEADLINE: 15/01/2019 23:00 - Europe/Brussels
  • LOCATION: Multiple locations, see work locations below.
  • TYPE OF CONTRACT: Temporary
  • JOB STATUS: Full-time
  • HOURS PER WEEK: approx. 40 (depending on the recruiting partner)

The Marie Curie Innovative Training Network "Machine Learning Frontiers in Precision Medicine" (MLFPM) brings together leading European research institutes in machine learning and statistical genetics, both from the private and public sector, to train 14 early stage researchers. These scientists will develop and apply machine learning methods to health data. The goal is to reveal new insights into disease mechanisms and therapy outcomes, and to exploit the findings for precision medicine, which hopes to offer personalized preventive care and therapy selection for each patient.

Besides working on their project at their home institutions, the researchers will participate in network-wide training events like summer schools and retreats. Moreover, they will conduct two secondments of three months each at other network partners.

Applicants with a background in Computer Science, Mathematics, Engineering, Medicine, Biology or related fields are encouraged to apply. We expect that applicants hold a university degree that qualifies them for doctoral studies at their recruiting organization.

MLFPM is striving for diversity. In particular, we are committed to increase the percentage of female scientists and therefore especially encourage them to apply.


The following 14 positions and projects are available:

ESR1 will work on "Machine Learning for Biological Network Analysis" with Karsten Borgwardt at ETH Zürich in Basel, Switzerland.

ESR2 will work on “Treatment optimisation with patient state Representations and inverse reinforcement learning” with Gunnar Rätsch at ETH Zürich in Zürich, Switzerland.

ESR3 will work on “Comparison of heterogeneous or uncertain network structures” with Kristel Van Steen at University of Liege, Belgium.

ESR4 will work on methods for subtype detection in high-dimensional data with a special focus on longitudinal data with Bertram Müller-Myhsok at the Max Planck Institute of Psychiatry in Munich, Germany.

ESR5 will work on “Deep representations of somatic mutations and germline variants for cancer Research” with Bernhard Schölkopf at the Max Planck Institute for Intelligent Systems in Tübigen, Germany.

ESR6 will work on “Clinical decision support for precision medicine” with Tobias Heimann and Volker Tresp at Siemens Healthcare GmbH in Erlangen, Germany.

ESR7 will work on "Methodology for discovery and validation of omics-based predictors for follow-up data in large population-based biobanks" with Krista Fischer at the University of Tartu in Tartu, Estonia.

ESR8 will be "Predicting patient trajectories and outcomes from national level data" with Jaak Vilo, Meelis Kull and Sven Laur at STACC Ltd in Tartu, Estonia.

ESR9 will work on “Machine learning for the discovery of new functional and regulatory gene networks” with Joaquin Dopazo at Fundación Pública Andaluza Progreso y Salud in Sevilla, Spain.

ESR10 will work on “Personalized health trajectories” with Antonio Artés at Universidad Carlos III de Madrid in Madrid, Spain.

ESR11 will work on learning from multi-modal data to improve cancer treatment with Chloé-Agathe Azencott at ARMINES/Mines ParisTech in Paris, France. For a more detailed description, see:

ESR12 will work on "Integration of multi-omics data and disease-related phenotypes for better disease risk prediction" with Florence Demenais at the University Paris Diderot, in Paris, France.

ESR13 will work on "Visualization of Deep Learning on Biomedical Data for Improved Interpretability" with Magnus Fontes at Qlucore in Lund, Sweden.

ESR14 will develop and apply causal inference methods to high dimensional healthcare data with Chen Yanover at the Healthcare Informatics Department in IBM Research - Haifa, Israel. See and for more details.


Recruitment requirements

At the time of their recruitment, candidates must be in the first four years (full-time equivalent research experience) of their research careers and have not been awarded a doctoral degree.

Moreover, candidates have to fulfill the mobility condition: they must not have resided or carried out their main activity (work, studies, etc.) in the country of the recruiting partner for more than 12 months in the 3 years immediately before the recruitment date.

All researchers will be enrolled in PhD programs, so they need to have a university degree that qualifies for PhD studies at the organization they apply to.

Candidates with a biological or medical background, without or with little mathematical background, are also encouraged to apply. Upon recruitment, mathematical preparation plans will be organized for these candidates where needed.


Working conditions

All beneficiaries will be full-time employed at their institution. Special family situations might qualify for part-time employment. The researchers are expected to conduct two secondments of three months each at other network partners.


Recruitment process

The board of the network will evaluate all applications, and the top-ranked candidates will be invited for interviews.


Aplication form and original source: