Postdoctoral Research Associate in Machine Learning (Job Number: 25000466)
Department of Computer Science
Grade 7: - £38,249 - £45,413 per annum
Fixed Term - Full Time
Contract Duration: 24 months
Contracted Hours per Week: 35
Closing Date: 14-May-2025, 5:59:00 PM
Disclosure and Barring Service Requirement: Not Applicable.

QUALIFICATIONS

• A good first degree in Computer Science, Machine Learning, Maths and Statistics, Robotics or a related subject.
• A PhD (or an MSc with extensive research experience) in Computer Science, Machine Learning, Maths and Statistics or a related subject.

EXPERIENCE

• Strong experience in conducting high quality academic research in deep learning, machine learning, computer vision and multi-modal data processing for real-world robotic applications.
• Strong background in deep learning, supervised and unsupervised learning, time-series analysis, information visualisation, with experience in developing and implementing very large deep learning models.
• Familiarity with high performance computing environments (e.g., HPC clusters, GPUs, Cloud resources) and managing Linux based hardware systems.
• Strong experience in programming for model development and designing experimental environments for deep learning applications using PyTorch, TensorFlow, JAX, etc.
• Demonstrable experience in data processing and analysis using state-of-the-art data science languages (e.g., Python) as well as experience in programming languages such as Python, C and C++.

SKILLS

• Demonstrable ability to present research papers at conferences and communicate complex information to specialists and within the wider academic community.
• Demonstrable ability to write material of a quality commensurate with publication in highly-ranked journals, evidenced by publication record.
• Ability to work independently on own initiative and to strict deadlines.
• Excellent interpersonal and communication skills.

EXPERIENCE

• Experience of working in a multidisciplinary team.
• Previous experience with deep learning and computer vision techniques applied to robotics, biological or ecological research, or environmental monitoring; including tasks such as object detection, segmentation and classification in complex, real-world settings.
• Familiarity with additional areas such as SLAM, 3D reconstruction, multi-modal data fusion and neuromorphic sensing or processing. A demonstrated ability to adapt AI techniques across domains and integrate them into real-time or embedded systems is highly desirable.

THE ROLE

The successful applicant will be responsible for the design, development, and implementation of deep learning and computer vision frameworks across a range of research projects. This includes developing and training deep learning models for tasks such as scene understanding, object detection, segmentation, classification, and pose estimation, as well as integrating these models into real-time systems. The role will involve working with large and multi-modal datasets (e.g., images, video, audio, and sensor data), and deploying solutions in real-world environments, particularly in robotics-focused applications. Familiarity with robotics concepts such as SLAM, sensor fusion, visual odometry and hardware integration is highly desirable. The candidate will be expected to contribute to experimental design, evaluation using benchmark datasets, and the development of reproducible and scalable solutions. A flexible and creative approach to problem-solving is essential, as projects may span a range of domains including environmental monitoring, autonomous navigation and intelligent perception systems.
Collaboration with our European partners is anticipated, and the successful candidate will be encouraged to adopt a creative approach to problem-solving, exploring various deep learning techniques. Verification of these models and algorithms will be conducted using benchmark datasets and real-world tests in diverse aquatic environments, necessitating a willingness to engage in experimental work for real-world verification with biologists and other stakeholders. This role will involve close collaboration with external partners across EU. You will be expected to visit several of partner institutions in the course of your work and will be based in the Department of Computer Science at Durham University.

KEY RESPONSIBILITIES:

• To conduct individual and collaborative research projects under the direction of the line manager.
• To design and develop novel and cutting-edge machine learning, computer vision and image processing frameworks for tasks such as object detection, segmentation, pose estimation, scene understanding and multi-modal perception.
• To contribute to the development and integration of AI models in real-world systems, including robotic applications involving SLAM, autonomous navigation, sensor fusion, and visual-inertial odometry.
• To explore the application of foundation models, including large vision-language models (VLMs) and large language models (LLMs), in enhancing perception, reasoning and decision-making capabilities.
• To understand and convey material of a specialist or highly technical nature to the team or group of people through presentations and discussions that leads to the presentation of research papers in conferences and publications.
• To prepare and deliver presentations on research outputs/activities to audiences which may include: research sponsors, academic and non-academic audiences.
• To publish high quality outputs, including papers for submission to peer reviewed journals and papers for presentation at conferences and workshops under the direction of the Principal Investigator or Grant-holder.
• To assist with the development of research objectives and proposals.
• To work with the line manager and other colleagues in the research group, as appropriate, to identify areas for research, develop new research methods and extend the research portfolio.
• To deal with problems that may affect the achievement of research objectives and deadlines by discussing with the Principal Investigator or Grant-holder and offering creative or innovative solutions.
• To liaise with research colleagues and make internal and external contacts to develop knowledge and understanding to form relationships for future research collaboration.
• To plan and manage own research activity, research resources in collaboration with others and contribute to the planning of research projects.
• To deliver training in research techniques/approaches to peers, visitors and students as appropriate.
• To be involved in student supervision, as appropriate, and assist with the assessment of the knowledge of students.
• To contribute to fostering a collegial and respectful working environment which is inclusive and welcoming and where everyone is treated fairly with dignity and respect.
• To engage in wider citizenship to support the department and wider discipline.
• To engage in continuing professional development by participation in the undergraduate or postgraduate teaching programmes or by membership of departmental committees, etc. and by attending relevant training and development courses.