Bar-Ilan University

Ref.: 534613 Work type: Full-time Department: School of Public Health (22400) Categories: Senior Research Staff & Post-doctoral Fellow Applications are invited for appointments as Post-doctoral Fellow (several posts) in the Division of Public Health Laboratory Sciences, School of Public Health (Ref.: 534613), to commence as soon as possible, on a one- to three-year temporary term contract, with the possibility of renewal subject to funding availability and satisfactory performance. Applicants should possess a Ph.D. degree in Biology or related disciplines, preferably with experience in virology research. They should have solid knowledge of chemistry and biology, and an excellent command of spoken and written English and Chinese. Experience in bioinformatic data analysis including microbiome, virome and environmental metagenomics would be preferred. They should be logical, detail-oriented and self-motivated. Preference will be given to those with experience in virus transmission. The appointees will lead a research team to conduct data analysis for different types of data, including genomic/metagenomic sequencing data, ecological surveillance data, as well as development of analysis pipeline. Working off-campus, outside office hours and on weekends may be required occasionally. Enquiries about the duties of the post should be sent to Mr Eric Yip at yiperic@hku.hk. Information about the School can be obtained at https://sph.hku.hk/. A highly competitive salary commensurate with qualifications and experience will be offered, in addition to annual leave and medical benefits. The University only accepts online application for the above posts. Applicants should apply online and upload an up-to-date CV, together with a cover letter and a list of relevant research publications. Review of applications will start as soon as possible and continue until June 4, 2026, or until the posts are filled, whichever is earlier.

Role Overview As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT. The SIT Centre for Intelligent Robotics is a strategic capability centre under RoboPrecinct@Punggol Digital District (PDD), responsible for developing and maintaining the core applied robotics and embodied AI capabilities required for precinct-level deployment of robotic systems. The centre is seeking a Robotics Engineer. This role is application-driven and system-oriented, focusing on the implementation, adaptation, and evaluation of state-of-the-art robotics algorithms on real robotic platforms. The successful candidate will work closely with physical robots and simulation environments to translate recent advances in learning-based robotics into robust, deployable systems. Key Responsibilities Implement, adapt, and evaluate state-of-the-art learning-based methods for robotic perception, decision-making, navigation, and manipulation. Apply reinforcement learning, imitation learning, and other skill learning frameworks to practical robotic applications. Integrate learning-based robot skills with full robot software stacks in ROS / ROS 2 environments. Develop and maintain perception and state representation pipelines using RGB-D and 3D sensing. Conduct simulation-to-real (Sim2Real) workflows, including data collection, model training, deployment, and testing on physical robots. Support system-level experimentation, benchmarking, and iterative improvement of embodied AI applications. Provide mentorship to undergraduate students in projects. Carry out risk assessment, and ensure compliance with Work, Safety and Health Regulations. Prepare technical documentation, reports, and implementation guides to support knowledge transfer and long-term maintainability. Contribute to publications and technical notes to disseminate research outcomes. The employee is to communicate with any internal or external stakeholders to ensure project deliverables are met. Any other adhoc duties assigned by supervisor. Requirements Bachelor’s degree or higher in Robotics, Computer Science, Mechanical Engineering, Control Engineering, or a related field. Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow. Working knowledge of reinforcement learning, imitation learning, or learning-based control concepts. Familiarity with robotics simulation tools such as Gazebo, Isaac Lab, or MuJoCo. Practical experience with ROS or ROS 2 for real-robot software integration. Proficiency in Python and C++, with experience developing and maintaining production-level code. At least 2 years of experience in robotics research or industry projects. Preferred Skillsets (Advantageous) Experience working with mobile manipulators, humanoid robots, or quadruped robots. Exposure to motion planning, control frameworks, or whole-body control systems. Experience with real-world robot experimentation and deployment. Experience with embodied AI and robot manipulation benchmarking platforms such as LIBERO, ManiSkill, or RLBench is highly desirable. Knowledge of GPU architectures, GPU cloud computing services, and strong familiarity with Linux operating systems. Knowledge of BIM, universal scene description and scene composition Knowledge of physics and simulation parameters, as well as sensor simulation pipeline.

At the heart of SIT’s mission is to nurture industry-ready graduates equipped with deep technical expertise and transferable skills to tackle tomorrow’s challenges. SIT collaborates with industry in our education, while benefitting them with our talent supply and collaborative research achievements. The University’s unique applied learning pedagogy integrates work and study, embedding authentic learning experiences within real-world environments. Through strategic partnerships forged by our faculty with industry, learners bridge theoretical knowledge with practical application in and out of the classroom. Such active & authentic learning opportunities ensures graduates step into their careers with applicable skills and competence.  In research, SIT faculty aims to bridge knowledge creation and utilisation. Our faculty collaborates closely with industry on translational research and innovation with focus on addressing pertinent industry challenges to impact businesses.  The Singapore Teaching and Academic Research Talent (START) Scheme is co-funded by the Ministry of Education (MOE) and SIT to encourage and support outstanding young Singaporeans interested in pursuing an academic career at SIT. The Early Career Awards (ECA) are designed to support Singaporeans who have recently completed their postdoctoral training and prepare them for an academic career at SIT. This award aims to enhance the teaching capabilities of the recipient and help build a strong research portfolio. Eligibility and Selection Criteria Singapore Citizen   Recently completed postdoctoral training   Excellent academic record plus a high degree of proficiency and motivation for a research area that is strongly aligned with SIT’s supported areas  Strong passion for teaching, research and an academic career Benefits Awardees may receive research grants to help build a competitive research portfolio. Upon completion of the award, awardees will be considered for Assistant Professorship. Awardees will also be assigned a faculty mentor for the duration of the scheme. Application Process Applications are open throughout the year. Applications received before 15 January 2026 will be considered for intake 2026.  Interview by the respective clusters for shortlisted applicants will be conducted throughout the year.  Final Interview by Panel for shortlisted applicants will take place from March to April 2026. Awards for intake 2026 will be finalised by July 2026. Supported Areas for START Food, Chemical and Biotechnology Sustainable and Functional food: Sustainable food systems & circular food economy - alternative proteins, upcycled ingredients); AI & automation in food manufacturing - smart sensors, digital twins, and AI-driven food quality control and processing; Food safety - supply chain tracking/monitoring, blockchain Biotechnology: Host strain screening and engineering for microbial bioprocess development; microbial physiology and adaptation for industrial applications; high-throughput screening and automation for strain optimization; bioprospecting for novel microbial hosts, enzymes, and metabolites to support sustainable biomanufacturing. (Bio)pharmaceuticals: Processing science and technology for bio- and chemical transformations, separations, purifications and formulation applied to pharmaceuticals manufacture; modelling of processing steps and whole processes to support innovation, design, optimisation and control for manufacturing operations. Process control: process modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital twins and real-time process monitoring and control; process analytical technology (PAT); process intensification and sustainability.