Topic: Multimodal learning

Develop machine-learning methods for generative drug discovery and turn research prototypes into usable scientific models. The role combines multimodal data, high-throughput experimentation and modern generative architectures, with work on model evaluation, production implementation and computational infrastructure. This is an on-site research position. Applications are available on the employer’s current vacancy page, with no fixed deadline stated.

Research models that connect chemical structures, protein sequences and cellular transcriptomics to predict biological responses. Develop and test scientific hypotheses using large single-cell perturbation datasets, contributing new architectures and evaluations for drug discovery. The employer lists the role as on-site with at least three office days each week. The exact role page currently accepts applications and states no fixed deadline.

Alex Smola discusses the development of voice agents, audiovisual systems and AI avatars. Topics include audio tokenization, inference cost, conversational latency, visual context and how artificial systems might learn from interaction while responding appropriately to human emotion.

Two postdoctoral positions in Naoto Yokoya’s Geoinformatics Team at RIKEN AIP develop multimodal and agentic AI for disaster-risk assessment and geospatial decision support. Research combines satellite, aerial, ground and simulation data with computer vision, machine learning and language models. Responsibilities include research publication, collaboration and support for junior researchers. The Tokyo appointment is renewed annually, subject to evaluation and project continuation, through March 2029 at the latest. Applications are accepted until the positions are filled; the official vacancy e