Identify and pursue new business opportunities within the commercial interior, renovation, and fit-out sector through proactive outreach and networking.
This position is a Shift Role (12 Hour) who need to work Day/Night Shift rotating by month. Trainee need to learn basic MES system operation and Defect Review images classification.
The trainee will undergo a structured 6-month on-the-job training programme within the Wet Process Engineering team in a 300mm semiconductor wafer fabrication facility. Designed for fresh Polytechnic graduates, the programme provides foundational exposure to semiconductor manufacturing, wet cleaning and wet etching processes, statistical process control (SPC), manufacturing data analysis, and process improvement methodologies. Under the guidance of experienced engineers, the trainee will develop technical competencies required for an Associate Engineer role while supporting daily manufacturing operations and continuous improvement initiatives.
Strategic Thinking: Ability to approach challenges and opportunities with a high-level, analytical mindset—synthesising information, anticipating implications, and developing effective ways of organising work, clarifying accountabilities, and guiding both planned strategies and ad hoc initiatives.
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
