Open Positions

Requirements

A master's degree or equivalent in Computer Science, Electrical Engineering, Applied Mathematics, or a closely related field, with a focus on areas such as machine learning, data science, or computational statistics.
A demonstrated interest or background in Artificial Intelligence/Machine Learning, particularly with a vision to innovate and contribute significantly to research in deep learning for time series and graph data analysis.
Proficiency in Python is mandatory. Familiarity with the Linux operating system and with common programming tools and environments (Git, SSH, Anaconda, VSCode/Pycharm, etc...) is also required.
A solid understanding of deep learning and experience with Pytorch and common data analysis libraries such as Pandas, scikit-learn, Seaborn, etc...
A proactive approach to learning and implementing new coding practices, with the ability to adapt to and utilize new frameworks and languages as dictated by evolving project demands.
Commitment to staying informed about cutting-edge developments in deep learning, time series analysis, and graph data processing, and the ability to cultivate a robust research methodology.
Excellent written and verbal communication abilities, with the skill to articulate complex concepts clearly and effectively.
Willingness to communicate research results through blogs and social media and to participate in open source projects.

Requirements

In addition to the requirements for the PhD position, a Postdoc applicant must possess:

A completed PhD in Computer Science, Mathematics, or Physics with a dissertation focused on machine learning, deep learning, or a directly related area.
A strong publication record in top journals and conferences such as NeurIPS, ICLR, ICML, AAAI, ICCV, and CVPR./li>
Documented capacity to independently design and execute research.
A team player mindset, with proven experience working collaboratively in interdisciplinary settings.
Participation in the peer review process and proof of engagement in the research community.

Optional qualifications

In addition, the following competencies will be positively evaluated:

Organization of workshops, tutorials, special sessions, special issues, or meetups.
Experience with writing grant proposals and the ability to secure research funding from various sources.
Proven ability to work in interdisciplinary teams and with industry partners, including experience in applying deep learning techniques to practical problems.
Ability to present research findings to both scientific peers and non-expert audiences.
Experience in teaching relevant courses and supervising Master's students in their research projects.
A research plan for the postdoctoral period, demonstrating how one's work will advance the field of deep learning for time series and graph data.