The University of Applied Sciences and Arts of Southern Switzerland (SUPSI) invites applications for one Scientific Collaborator in the area of Deep Learning and/or NLP at the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) within the Department of Innovative Technologies (DTI), located in Lugano. The degree of employment is 100%, with a contract starting on 01.09.2026, or on a mutually agreed date.
Duties and responsibilities
The selected candidate conducts advanced research in Machine Learning, with a focus on natural language processing (NLP) and Deep Learning applications for the financial industry. The successful candidate will collaborate within an R&D team that targets the development of Deep Learning applications for the financial industry, in strong collaboration with other Machine Learning engineers, researchers and industry partners.
The candidate will:
- Develop and implement Deep Learning systems in a financial industry context.
- Cooperate in R&D teams to execute projects related to the financial industry.
- Collaborate with interdisciplinary teams to integrate NLP and Deep Learning technologies into practical applications.
- Prototype AI systems by applying state-of-the-art techniques in Deep Learning, Transformers, Large Language Models (LLM), AI agents, and AI models fine-tuning.
- Perform data analysis and reporting using Data Science techniques and methods.
Requirements
Essential Requirements
- Master’s degree in computer engineering, Machine Learning engineering, or equivalent background.
- In-depth knowledge of programming languages and scripting languages, such as Python and Bash.
- Experience with modern ML tools.
- Experience with Deep Learning libraries (e.g., PyTorch), Pandas, and NumPy.
- Good relational skills and ability to work both autonomously and in groups, in a goal-oriented environment.
- Good organizational and problem-solving skills.
- Good knowledge of spoken and written English, and Italian.
- The candidate should be among the top 10 % of Master graduates of their university.
Preferred Qualifications
- Extensive experience with Deep Learning, particularly in the context of LLMs.
- Experience with Reinforcement Learning, particularly in the context of LLMs.
- Experience with relational databases (e.g., MariaDB, PostgreSQL), NoSQL databases (e.g., MongoDB), graph database (e.g., Ne4J).
- Experience in using containerization tools (e.g., Docker, Apptainer).
- Experience in CI/CD tools (e.g., GitLab CI/CD, GitHub actions).
- Knowledge of software architectures like microservices, distribute systems, web services, REST APIs.
- Development of AI Agents.
Desired but not strictly required:
- Experience of work and/or research in a financial context.
- Research experience.
- PhD in Machine Learning or equivalent.
- Experience with HPC cluster computers.
- Knowledge of additional languages such as Java, C++, Rust, R.
- Knowledge of DevOps and MLOps methodologies.
- Knowledge of a second national language.
What SUPSI Offers
SUPSI offers a stimulating environment and unique opportunities for those who wish to grow professionally and actively contribute to innovation. Specifically, the position offers:
- Fixed-term employment contract.
- Attractive salary, in line with Swiss standards and the candidate's experience.
- An international working environment, with high-level academic and industrial collaborations.
- Participation in cutting edge research to bring top notch research partners in the financial industry forward.
- No obligation to teach or participate in university management.
- Sufficient time to pursue research work in collaboration and publication.
Application Process
Applications will only be considered if submitted electronically by 30.06.2026, through the dedicated application form. The following documents, written in English, must be included:
- Curriculum vitae.
- Motivation letter, including a brief description of past experience (1-2 pages).
- Names and email addresses of three reference contacts.
For further information, please contact Dr. Oleg Szehr.
Incomplete applications, those submitted to incorrect addresses, or those received after the deadline will not be considered. In addition to the required application documents, further documentation (such as a criminal record certificate or a copy of an identity document) may be requested at a later stage.
