Academic Positions at School of Digital Science (SDS)

Position / Area 

• Assistant Professor | Lecturer 

The appointee will prioritize teaching and curriculum development within the Data Science Major, focusing on the application of computational methods to enterprise decision-making, digital innovation and product marketing. Key responsibilities, but not limited to, include designing and delivering core modules, supervising undergraduate and graduate students, developing specialized labs for big data analytics and validation-driven prototyping, as well as building strong collaborations with the digital industry and government agencies to support practical real-world projects.  

Applicants without a PhD, however with relevant proven track record in the industry may be considered.  

Preferred Qualifications:  

• PhD in Information Systems (IS), Business Analytics, Computer Science (with an emphasis on Data/IS), or a closely related quantitative and computational field.   

• Undergraduate Degree (B.S.) and Master’s Degree (M.S.) in a discipline demonstrating strong analytical and technical proficiency, such as:   

• Computer Science    

• Data Science / Statistics   

• Operations Research   

• Demonstrated excellence and dedication to teaching technical topics at the university level, specifically in areas such as:   

• Agile Prototyping and Lean Product Development  

• Digital Marketing Analytics (e.g., attribution, SEO/SEM data, conversion optimization)  

• Data Modeling for BI (e.g., dimensional modeling, Kimball methodology)  

• Data-Driven Product Management lifecycle and metrics (e.g., A/B testing, North Star metrics).  

• Pay-per-Click (PPC) Advertising Platforms (e.g. Google Ads, Bing Ads, Apple Ads, Reddit Ads, etc.)  

•  Hands-on proficiency with major data/cloud platforms (e.g., AWS, Azure, Google Cloud) and modern programming languages (e.g., Python, R, SQL) for analytics.   

•  Evidence of effective supervision and mentoring of undergraduate and/or graduate student projects.  

Preferred Qualifications:  

• Established research trajectory in areas such as recommender systems, optimizing digital customer journeys, the use of AI in marketing personalization, or the economics of digital platforms.  

• Practical experience leading data product development teams, managing CRM/Marketing Automation systems, or implementing large-scale data governance frameworks in industry.  

• Experience in developing and delivering courses related to Data Privacy (e.g., GDPR/CCPA implications) and Digital Ethics.  

• Proven ability to secure external funding (grants or industrial contracts) to support applied research and infrastructure development.  

• Professional certifications (e.g., Certified Scrum Product Owner, relevant cloud or analytics certifications).  

Application Materials (Submit):  

• Cover letter (detailing pedagogical philosophy and research interests)  

• Curriculum vitae with Google Scholar, Scopus, ORCID links  

• Research statement (2–3 pages, concisely outlining future direction in Data/IS)  

• Detailed Teaching Statement and Portfolio (including course materials, student feedback, and evidence of teaching effectiveness)  

• Names and contact information for 3 references  

• Representative publications (up to 3)  

• Assistant Professor | Lecturer 

• teach courses in areas of Cybersecurity and Digital Forensics in programmes offered by the School of Digital Science, and modules offered by other Faculties, Institutes and Centres (FICs) in Universiti Brunei Darussalam;  

• develop frameworks for learning and the design of instruction, and improve assessment methods in related module(s) in various settings (contexts);  

• publish research advances in top-tier conferences and journals, and demonstrate leadership in the greater professional community;  

• execute research projects in close collaboration with academics and professionals within and outside UBD to make a global impact through excellence in research and innovation and active participation in the activities of the School of Digital Science;  

• supervise or co-supervise final year and postgraduate students from the School as well as from different faculties in projects related to Cybersecurity, Digital Forensics, and Cyber Investigations; and  

• provide administrative support to the School of Digital Science (if required).  

Academic and Professional Qualifications Required:  

• Ph.D. in Computer Science, Cybersecurity, Digital Forensics, or a related discipline from a recognised university;  

• an outstanding record of research productivity and teaching excellence in the field of specialisation;  

• preference will be given to those who have taught (and supervised research students) at graduate level; and  

• applicants with experience and expertise in digital forensics and cybersecurity platforms and tools, such as disk and memory forensics tools, incident response frameworks, malware analysis environments, network security tools, cloud forensics platforms, and cyber investigation systems, will be preferred.  

Application materials (submit):  

• Cover letter (including teaching and research vision)  

• Curriculum vitae with Google, Scopus, ORCID links   

• Research statement (3–5 pages)  

• Teaching statement and evidence of teaching effectiveness  

• Names and contact information for 3 references  

• Representative publications (up to 3)  

For more information about the School of Digital Science, please refer to: http://sds.ubd.edu.bn/    

• Assistant Professor | Lecturer 

Applicants must demonstrate a track record in research. Teaching experience, both at undergraduate and post graduate levels, is an added advantage, and successful candidate is expected to participate in the teaching and development of undergraduate and graduate courses, including the supervision of undergraduate and graduate research students.  

The successful candidate will be responsible in the development and operation of the software development training programs catering for lifelong learning students.  Successful applicant is also expected to participate in administrative activities of the faculty. 

• Assistant Professor 

Required qualifications: 

• PhD in Computer Science, AI, Computational Linguistics, Machine Learning, or a closely related field.  

• Demonstrated research potential in NLP, knowledge graphs and graph-based methods as evidenced by publications in top-tier venues (ACL/ICLR/NAACL/NeurIPS/ICML/UAI/etc.).  

• Experience or clear potential for high-quality university teaching and student supervision.  

• Solid software skills (e.g., Python, PyTorch/TensorFlow), experience with graph libraries (e.g., DGL, PyG, RDF/SPARQL stack) and practical ML engineering for experiments.  

 Preferred qualifications: 

• Research on statistical NLP, link prediction, language models, graph neural networks, knowledge representation, or hybrid symbolic–neural systems.  

• Track record or promise of securing external funding and collaborating across disciplines (healthcare, climate & biodiversity).  

• Experience with ethical/responsible AI practices, privacy-aware NLP, or domain adaptation/low-resource methods.  

 Application materials (submit): 

• Cover letter (including teaching and research vision)  

• Curriculum vitae with Google, Scopus, ORCID links   

• Research statement (3–5 pages)  

• Teaching statement and evidence of teaching effectiveness  

• Names and contact information for 3 references  

• Representative publications (up to 3)  

• Assistant Professor | Lecturer 

Required qualifications:  

• PhD in Robotics, Computer Science, Electrical Engineering, or related field.  

• Established research record in perception and manipulation with highquality publications and evidence of independent scholarship as evidenced by acquired research grants and publications in top-tier venues (ICRA/CVPR/IROS/NeurIPS/ICML/UAI/etc.).  

• Demonstrated experience supervising PhD students and leading research teams.  

• Strong experimental background with handson experience on robot arms and sensing suites; proficient in ROS/ROS2, and mainstream ML frameworks (PyTorch/TensorFlow).  

• Proven teaching ability at undergraduate and graduate levels.  

Preferred qualifications:  

• Track record of securing external funding and leading multidisciplinary projects.  

• Experience with tactile sensing, compliant control, dexterous hands, or humanrobot collaboration.  

• Demonstrated work on simtoreal methods, domain randomization, or realworld benchmark contributions.  

• Industrial collaboration experience or translational impact (deployments, prototypes, spinouts).  

• Leadership/service experience (programme development, lab management, conference organization, editorial roles).  

Application materials (submit):  

• Cover letter (including teaching and research vision)  

• Curriculum vitae with Google, Scopus, ORCID links   

• Research statement (3–5 pages)  

• Teaching statement and evidence of teaching effectiveness  

• Names and contact information for 3 references  

• Representative publications (up to 3)  

• Assistant Professor  

Required qualifications:  

• PhD in Computer Science, Machine Learning, AI, or closely related field.  

• Strong research record in generative models, LLMs, or related areas as evidenced by publications in top-tier venues (ACL/CVPR/ICLR/NeurIPS/ICML/UAI/etc.).  

• Demonstrated practical experience deploying ML/LLM systems (model serving, scaling, CI/CD, monitoring).  

• Proficiency with modern ML frameworks (e.g. PyTorch/TensorFlow), inference/serving stacks (e.g. Ray Serve, Triton, FastAPI), and cloud/container tech (e.g. Docker, Kubernetes).  

• Evidence of, or clear potential for, highquality university teaching and graduate supervision.  

Preferred qualifications: 

• Established record of independent, highquality research in LLMs/generative AI with firstauthor publications in leading venues and evidence of scholarly leadership.  

• Experience with responsible AI practices: model evaluation, bias/fairness auditing, redteaming, and safety pipelines; and application of parameterefficient finetuning (LoRA, adapters), quantization, distillation, and latency/cost optimizations.  

• Background in privacypreserving techniques (federated learning, differential privacy) or edge deployment.  

• Track record in securing external funding, leading multidisciplinary or industrypartnered projects.  

• Experience producing reproducible codebases, datasets, and deployment demos for teaching and research.  

Application materials (submit): 

• Cover letter (including teaching and research vision)  

• Curriculum vitae with Google, Scopus, ORCID links   

• Research statement (3–5 pages)  

• Teaching statement and evidence of teaching effectiveness  

• Names and contact information for 3 references  

• Representative publications (up to 3)  

• Assistant Professor | Lecturer 

• teach courses in areas of Blockchain Technology in programmes offered by the School of Digital Science, and modules offered by other Faculties, Institutes and Centres (FICs) in Universiti Brunei Darussalam;   

• develop frameworks for learning and the design of instruction, and improve assessment methods in related module (s) in various settings (contexts);   

• publish research advances in top-tier conferences and journals, and demonstrate leadership in the greater professional community;   

• execute research projects in close collaboration with academics and professionals within and outside UBD to make a global impact through excellence in research and innovation and active participation in the activities of the School of Digital Science;  

• supervise or co-supervise final year and postgraduate students from the school as well as from different faculties in projects related to Blockchain Technology; and  

• provide administrative support to the School of Digital Science (if required).  

Academic and Professional Qualifications required:  

• Ph.D. in Computer Engineering, Computer Science, or a related discipline (with a specialisation in Blockchain Technology) from a recognised university.  

• An outstanding record of research productivity and teaching excellence in the field of specialisation;  

• Preference will be given to those who have taught (and supervised research students) at graduate level; and  

• Applicants with experience and expertise in Blockchain Technology related platforms such as Ethereum, Ripple, Hyperledger Fabric, Tron, Bitcoin, Stellar etc., will be preferred.  

For more information about the School of Digital Science, please refer to: http://sds.ubd.edu.bn/  

• Assistant Professor | Lecturer 

The applicant should hold a PhD in Computer Science or a closely related discipline.  Prior teaching experience of advanced-level undergraduate courses in enterprise architecture, distributed system and computer science will be an advantage.  The successful applicant will be expected to teach undergraduate and/or graduate level courses in the computer science program, and to supervise final year student projects as well as graduate students.   

The successful applicant will be expected to develop new module(s) in enterprise architecture.  The successful applicant will be expected to continue active research in the field of enterprise architecture with existing members of the computer science group in UBD.  

• Assistant Professor | Lecturer 

Required Qualifications:  

• PhD in Computer Science, Computational Finance, Financial Technology (FinTech), Financial Engineering, Applied Mathematics, or a closely related quantitative and computational field.  

• Undergraduate Degree (B.S.) and Master’s Degree (M.S.) in a highly quantitative discipline such as:  

• Computer Science / Software Engineering  

• Applied Mathematics / Statistics  

• Physics / Engineering  

• Quantitative Finance / Financial Engineering  

• Demonstrated excellence and dedication to teaching computational topics at the university level, specifically in areas such as:  

• Programming for Finance (e.g., Python, C++)  

• Data Structures and Algorithms applied to financial systems  

• Financial Data Analytics and Machine Learning.  

• Strong background in curriculum design, pedagogical innovation, and developing practical, project-based learning experiences (e.g., teaching with financial datasets, algorithmic trading simulations).  

• Hands-on experience developing or managing specialized educational computing labs, high-performance computing teaching testbeds, or sophisticated financial simulation environments for student use (e.g., Bloomberg terminals, simulated exchange APIs).  

• Evidence of effective supervision and mentoring of undergraduate and/or graduate student research projects.  

• Assistant Professor | Lecturer 

Required qualifications:  

• PhD in Computer Science, Physics, Applied Mathematics, Electrical Engineering, Data Science, or a closely related field.  

• Demonstrated research record in quantum algorithms, quantum machine learning, or quantum optimization with publications in reputable venues.  

• Experience with quantum programming frameworks (e.g. Qiskit, Cirq, Pennylane, Braket) and classical ML/datascience tools (e.g., PyTorch, TensorFlow, scikitlearn, JAX).  

• Evidence of effective teaching and graduate supervision.  

Preferred qualifications:  

• Track record of securing external research funding or clear plan to obtain funding.  

• Demonstrated ability to translate quantum methods to applied datascience problems (finance, chemistry, healthcare, logistics) and to collaborate across disciplines.  

• Experience building reproducible research artifacts, teaching labs that use cloud QPUs/simulators, and producing open datasets/code.  

• Familiarity with hybrid quantumclassical workflows, noiseaware algorithm design, and benchmarking/verification methods.  

Application materials (submit):  

• Cover letter (including teaching and research vision)  

• Curriculum vitae with Google, Scopus, ORCID links   

• Research statement (3–5 pages)  

• Teaching statement and evidence of teaching effectiveness  

• Names and contact information for 3 references  

• Representative publications (up to 3)  

• Assistant Professor | Lecturer 

Required qualifications:  

• PhD in Robotics, Electrical/Electronic Engineering, Computer Science, Control Systems, or a closely related field.  

• Demonstrated research excellence in UAV/UUV autonomy, multiagent systems, or closely related areas (strong publication record).  

• Handson experience with field deployments, HIL/SITL testing, or managing experimental testbeds.  

• Proficiency with ROS/ROS2, flight stacks (PX4/ArduPilot), simulation tools (Gazebo, Ignition, Isaac, AirSim), and common toolchains (MAVLink, QGroundControl).  

• Evidence of effective teaching and graduate supervision.  

Preferred qualifications:  

• Proven success securing external funding and leading multidisciplinary teams.  

• Experience with communication-aware/decentralized control, consensus algorithms, and resilience under adversarial/uncertain environments.  

• Background in safety, verification, and regulatory aspects (airspace/maritime permissions, BVLOS operations).  

• Experience developing reproducible simulation-to-deployment pipelines and open research artifacts (datasets, benchmarks, code).  

• Industrial collaborations, technology transfer, or field-program leadership experience.  

Application materials (submit):  

• Cover letter (including teaching and research vision)  

• Curriculum vitae with Google, Scopus, ORCID links   

• Research statement (3–5 pages)  

• Teaching statement and evidence of teaching effectiveness  

• Names and contact information for 3 references  

• Representative publications (up to 3)