École Normale Supérieure

Full time fixed term for 2 years at Cobbitty, Camden Campus Opportunity to conduct research applying computational biology and AI modelling to plant-pathogen interactions Base Salary $113,400 - $121,054 + 17% superannuation About the opportunity The School of Life and Environmental Sciences (SOLES) at the University of Sydney is seeking to appoint a Postdoctoral Fellow in Computational Biology and AI Modelling to be based at the Plant Breeding Institute on the University's Camden Campus. SOLES has an international reputation for education and research, with well-funded external research collaborations across industry. The University of Sydney has significant strengths in plant breeding, crop production, plant biology and genetics, and agricultural robotics. This role offers an exciting opportunity for a highly motivated computational biologist to join a multidisciplinary research team investigating plant-pathogen interactions through integrative computational modelling. You will develop and apply quantitative and data-driven models to study protein interactions and their context-dependent behaviour, linking computational predictions with condition-specific biological responses. You will have access to large-scale biological datasets, high-performance and cloud-based computing resources, and work in close collaboration with experimental biologists and external computational partners. The successful applicant will demonstrate clear potential to become a leader in their field, with a strong research trajectory focused on computational modelling of molecular interactions and regulatory processes to generate mechanistic insight into host-pathogen biology. Your research interests will complement and/or enrich existing strengths in the Institute and School in plant breeding, molecular biology, genetics and pathology. You will contribute to current research programs, particularly in resistance gene and avirulence gene cloning, functional studies, and host-pathogen interactions, and articulate a clear vision for collaboration within SOLES, across the University of Sydney and beyond. Your key responsibilities will be to: develop and apply computational models to investigate protein-protein and protein-ligand interactions in plant-pathogen systems integrate diverse omics datasets to infer molecular interactions and regulatory processes design, implement and maintain reproducible, scalable computational pipelines for large biological datasets optimise workflows for high-performance computing (HPC) and cloud-based environments collaborate closely with experimental research teams to translate model outputs into testable biological hypotheses contribute to scientific documentation, including high-impact publications, research grant applications and reports present research findings at national and international scientific conferences. About you The University values courage and creativity; openness and engagement; inclusion and diversity; and respect and integrity. As such, we see the importance of recruiting talent aligned to these values and are looking for a Postdoc Research Associate - Computational Biology who has: a PhD in bioinformatics, computational biology, systems biology, or a related discipline strong proficiency in Python and R for scientific computing and pipeline development demonstrated experience in quantitative, statistical or data-driven modelling of biological systems experience developing reproducible workflows in HPC and/or containerised environments strong analytical capability, scientific ownership and a track record of high-quality research publications a clear vision for how your research complements and/or enriches the strengths of the School and Institute demonstrated experience in successful collaborative research projects (advantageous) excellent written, oral and interpersonal communication skills, with strong time management capability. Desirable experience familiarity with structure-informed or interaction-prediction modelling approaches experience integrating computational analyses with experimental or functional validation demonstrated experience in protein-centric analyses, including protein interaction networks, signalling pathway modelling, proteomics or protein target screening background or demonstrated interest in plant biology or plant-pathogen molecular interactions familiarity with molecular cloning techniques. To keep our community safe, please be aware of our COVID safety precautions which form our conditions of entry for all staff, students and visitors coming to campus. Work Rights You must have unrestricted work rights in Australia for the duration of this employment to apply. Visa sponsorship is not available for this appointment. Pre-employment checks Your employment is conditional upon the completion of all role required pre-employment or background checks in terms satisfactory to the University. Similarly, your ongoing employment is conditional upon the satisfactory maintenance of all relevant clearances and background check requirements. If you do not meet these conditions, the University may take any necessary step, including the termination of your employment. EEO statement At the University of Sydney, our shared values include diversity and inclusion and we strive to be a place where everyone can thrive. We are committed to creating a University community that reflects the wider community that we serve. We deliver on this commitment through our people and culture programs, as well as key strategies to increase participation and support the careers of Aboriginal and Torres Strait Islander People, women, people living with a disability, people from culturally and linguistically diverse backgrounds, and those who identify as LGBTIQ. We welcome applications from candidates from all backgrounds. How to apply Applications (including a cover letter, CV, and any additional supporting documentation) can be submitted via the Apply button at the top of the page. For employees of the University or contingent workers, please login into your Workday account and navigate to the Career icon on your Dashboard. Click on USYD Find Jobs and apply. For a confidential discussion about the role, or if you require reasonable adjustment or any documents in alternate formats, please contact Deepna Chand, Recruitment Operations by email to recruitment.sea@sydney.edu.au . © The University of Sydney The University reserves the right not to proceed with any appointment. Click to view the Position Description for this role. Applications Close Monday 27 April 2026 11:59 PM

As a University of Applied Learning, Singapore Institute of Technology (SIT) works closely with industry partners to develop applied research capabilities that translate directly into real-world deployment. Our research staff are equipped with industry-relevant skills through hands-on work on operational research platforms and systems. The Future Ship and System Design (FSSD) programme aims to develop strategic and innovative design capabilities for the maritime industry in Singapore and globally. We are seeking a certified Power Electrical Engineer to design, analyse, and operate high-power and high-voltage electrical systems, ensuring safe, reliable, and compliant operation across our facilities. The candicate will work closely with the Principal Investigator (PI), Co-PI, and research team members to manage and execute the project, ensuring that all deliverables are successfully achieved. Key Competencies Design and review high-power electrical systems, including switchgear, transformers, generators, and protection schemes Perform power system studies such as load flow, short-circuit, and protection coordination Operate and control high-power or high-voltage equipment Lead testing, commissioning, and troubleshooting of electrical systems Ensure compliance with electrical safety rules, standards, and regulatory requirements Support maintenance planning, reliability improvement, and asset management Work independently, as well as within a team, to ensure proper operation and maintenance of equipment The employee is to communicate with any relevant internal or external stakeholders to ensure project deliverables are met. Any other adhoc duties assigned by supervisor. Job Requirements Bachelor’s degree in Electrical Engineering or equivalent Certification or authorisation to design and operate high-power or high-voltage electrical systems Minimum 2 years of relevant experience Key Competencies  Good knowledge in electrical equipment and power system design. Good knowledge in reliability analysis. Strong knowledge of HV and LV power systems and protection Experience with power system analysis tools Strong safety mindset and clear technical communication skills

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.