We have an opening for a full-time post-doc position on a research project on the development of generative models for discrete data structures, such as graphs and in particular molecules. We seek to develop generative models capable of conditional generation as well as what can be considered the equivalent of style transfer for discrete structures. We expect to focus on generative models with latent variables since these allow us to actively manipulated the learning instances as well as to explicitate the available domain knowledge. Of particular interest are discrete latent spaces and the development of generative models that work natively over discrete data structures and such discrete latent spaces.
The project is funded from a grant of the Swiss National Science Foundation with funding secured for up to four years.
We seek strongly motivated candidates dedicated to high quality research. Candidates should have (or be close to obtaining) a Phd in machine learning, ideally in the area of generative modelling, and a strong research track-record attested by high quality publications in relevant machine learning venues such as ICML, NeurIPS, ICLR, AI-STATS, UAI, etc. The selected candidate is expected to demonstrate a high degree of independence and autonomy, drive their own research and actively contribute to the scientific development of the group through their knowledge and expertise as well as by proposing and contributing to group activities such as readings, schools, workshops etc. They are expected to participate in the supervision of a PhD student that will also be working within the same project. There is also a possibility to participate to teaching activities.
The DMML group consists of around ten researchers at the PhD and Post-doc level, working in different areas of machine learning, such as generative models, imitation learning. The team collaborates closely with the VIPER group, https://viper.unige.ch, from the computer science department of the University of Geneva headed by Prof. Stephane Marchand-Maillet. In addition the group is involved in a number of national and international research projects. We offer ample opportunities and support for scientific development, e.g. providing funding for conferences, schools, research visits and exchanges etc. We strive to provide a research environment in which researchers can focus on their research and allow for space and time to develop solid ideas.
If interested, please send the following to alexandros.kalousis@hesge.ch
- academic CV (max 2 pages)
- pointers to their two most important publications
- one page motivation letter explaining why the candidate is suitable for the position and what they can bring to the DMML group
- 1000 word research proposal on the research topic of generative models for discrete data structures
- contact details of three referees (do not send reference letters)
- copies of diplomas (PhD, MSc, BSc) and academic transcripts
Deadline for applications: 30/06/2022.
In case of any further questions, please contact
alexandros.kalousis@hesge.ch.