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Bring your research and consulting expertise into MetaBrain solution development. Turn industry knowledge and advisory methods into reusable, continuously updated software-enabled services.
Prompt Engineering Specialist – with deep expertise in crafting prompts for tools like ChatGPT, Gemini, Claude, and other large language models / generative AI systems.
Source and evaluate overseas Tier III/Tier IV data centre colocation resources, particularly facilities suitable for high-power GPU servers. Assess power capacity, PUE, electricity, bandwidth and other key specifications.
Adapt and train VLMs to read 2D engineering drawings: dimensions, tolerances, GD&T frames, surface finish, title blocks, bills of materials, notes, and section and detail views.
Own the full product lifecycle for assigned AI products: problem discovery, opportunity sizing, solution design, build prioritisation, launch, adoption tracking, and iterative improvement — with clear accountability for measurable business outcomes.
Project backgroundThe AI in drug discovery (AIDD) project focuses on developing innovative tools and technologies to accelerate drug discovery, with a particular emphasis on unlocking new druggable spaces, such as RNA-targeting molecules, for example. With AI-driven strategies, AIDD aims to accelerate target identification to lead discovery while also advancing novel small molecules and other therapeutic modalities for next-generation drug development.This project aims to develop novel deep learning methods for RNA tertiary structure prediction, inspired by the breakthroughs of AlphaFold in protein structure modeling. We plan to design a robust framework that incorporates RiNALMo, our state-of-the-art RNA language model [ Penić et al., 2025 ]. Additionally, we will investigate the integration of chemical reactivity measurements to enhance accuracy. Such data, closely tied to RNA’s 3D structure, offers valuable information on secondary structure elements, base-pairing, and conformational flexibility. By leveraging these inputs, our approach seeks to bridge the gap between computational and experimental methods, with significant implications for RNA drug discovery.Job descriptionWe are looking for a highly motivated postdoctoral researcher to:- Develop deep learning-based models for RNA structure prediction- Analyze chemical reactivity experimental data- Incorporate chemical reactivity experimental data into the structure prediction pipeline- Run large-scale training on high-performance computing infrastructure- Perform model finetuning and hyperparameter optimization- Evaluate models on experimental dataProfileWe welcome applications from candidates with:- A PhD in computer science, computational biology, computational chemistry, applied mathematics, physics, or a related field- Proven experience in deep learning research and development- Publication record at top-tier AI conferences (e.g., NeurIPS, ICLR, ICML, CVPR, ICCV, ACL, etc)- Strong experience in Python programming and solid software engineering skills- Experience with biomolecules and/or high-performance computing is a plus- Interest in biology, biomolecules, or genomics (prior expertise not required)- A structured, independent, proactive and collaborative working style