The "Integrated and Wireless Systems” Business Unit, based in Neuchâtel, Switzerland is looking for a PHD student.

Your mission

We invite applications for a fully funded PhD position in Multimodal Artificial Intelligence, focusing on the development of next-generation AI methods that integrate diverse data modalities while remaining deployable on resource-constrained hardware platforms. The successful candidate will work on novel multimodal fusion methods, cross-attention architectures, and robust representation learning, with a strong emphasis on hardware awareness, efficient deployment, and model generalization.

The project explores how imaging, sensor signals, structured clinical or contextual data, and temporal information can be combined into unified, interpretable, and transferable AI systems. Beyond algorithmic development, the research will investigate how these models behave under domain shifts and how they can be distilled into compact, deployable architectures suitable for edge devices.

Your responsibilities

• Develop algorithms for integrating heterogeneous data types (e.g., imaging, signals, text, structured metadata).
• Design cross-attention, transformer-based, or graph-based models to capture cross-modal dependencies.
• Build spatio-temporal models for data streams with both structural and temporal dimensions.
• Create distillation pipelines where large multimodal models supervise smaller, hardware-efficient models.
• Investigate cross-modal distillation strategies that preserve multimodal semantics in compressed models.
• Innovation and Research: conduct in-depth research to advance the state-of-the-art in embedded training, exploring novel approaches and solutions.
• Industry Technology Transfer: Work on projects with the primary goal of transferring developed technologies and innovations to the industry, bridging the gap between academia and practical applications.
• Publication: disseminate research findings by publishing in top-tier academic journals and presenting work at prestigious conferences, contributing to the field's knowledge and recognition.
• Support proposal writing.
• Supervision of master students.

Your profile

Know-how

• Master’s degree in computer science, Electrical Engineering, Machine Learning, or a related field.
• Strong background in machine learning algorithms and deep learning.
• Experience with attention (cross) mechanism
• Proficiency in programming languages such as Python, C++, and frameworks like TensorFlow or PyTorch.
• Research Aptitude: demonstrated research experience through coursework or prior projects and show eagerness to engage in research activities and a willingness to learn and contribute to ongoing projects.
• Academic Excellence: strong academic record, with a history of outstanding performance in relevant coursework.

Interpersonal skills

• Natural curiosity, adaptability, collaborative approach.
• Autonomous, innovative thinker, problem-solving abilities, results oriented.
• Strong organisation and communication skills.

CSEM mission and values

Our mission is the development and transfer of innovative technologies to the Swiss industry. Our objective is to make an impact on our customers and on society at large in the fields of precision manufacturing, digital technologies and sustainable energy. Our strength is the excellence of our people, about 650 passionate specialists dedicated to innovation and technology transfer. We believe that strong values support the successful development of our organization as well as the harmonious and balanced development of all our employees.

We are

• A unique place between research and industry at the cutting edge of new technologies
• An innovative, non-profit, and employee-driven company
• A dynamic, multidisciplinary, and multicultural environment
• A solar team focused on enabling solutions to energy challenges for a sustainable world

Working@CSEM means

• being part of a passionate community
• incredible flexibility, attractive working conditions, and great opportunities of development
• benefit from a management style based on trust & feedback and that favors a work-life balance

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity.
We look forward to receiving your complete application file via (CV, cover letter, certificates & diplomas) our job page. Preference will be given to professionals applying directly.