Total 46 data entry job vacancies in Education / Training category in Australia

Enjoy interacting with people and representing brands?

We are looking for individuals to join our team as Brand Representatives, focusing on customer engagement and brand awareness.

Job Description

• Conduct online research on prospective donors using public sources to help identify and assess new individuals and corporates who could donate, and help compile, proof, and edit in-depth profiles/reports on potential and existing donors for colleagues in the team.

• Input up-to-date research findings and prospect data into client's donor database in line with department’s data processes and standards. 

• Help prepare larger data sets for data-analytics within the department.

• Other database and research admin connected to prospects and donors. 

Requirements

• Minimum A-Levels/Diploma qualification 

• Possess experience working on databases and reviewing, analysing, and collating quantitative data or qualitative information from public sources. 

• Possess some research experience and skills (e.g. academic/study research, market research, fundraising research), to be able to review and analyse publicly available data (e.g. financials, philanthropic interests, and biographical information) and compile findings in a clear and concise manner.

• Good written communication skills and ability to produce quality profiles/reports to deadline.  

• Knowledge of MS Office applications including MS Excel and Word. 

• Ability to work independently or in a team and manage multiple tasks simultaneously.

• Demonstrate resourcefulness and flexibility.   

Contract duration: 6 months (renewable, subject to performance) 

Working Hours

• Mon – Thurs: 8.30 am – 5.45 pm
• Fri: 8.30 am – 5.15 pm
• Work-From-Home arrangement: One day per week week  

Salary: $2,600 - $3,000 per month

EA License No.: 96C4864 (Shanice Lim Xin Ni)

Reg. No.: R22110485

Established in 1992, the Wee Kim Wee School of Communication and Information (WKWSCI) is the only full-fledged communication school in Singapore and is a world-class centre of excellence with an international faculty, state-of-the-art research laboratories, and global institutional affiliations.

We are looking for a Research Engineer I / Research Engineer II (1-year contract) to support a research project examining disinformation and propaganda operations across Southeast Asia and their impact on Singaporeans. The project will focus on the digital space, including social media and messaging apps.

Key Responsibilities:

• Collect, scrape, and clean large datasets from online platforms and media sources.

• Conduct computational analysis of disinformation and propaganda networks.

• Develop and apply data visualization techniques to present findings clearly.

• Collaborate on research design and methodology discussions.

• Co-author academic papers, conference submissions, and project reports.

• Support literature reviews and integrate computational insights into theoretical frameworks.

• Assist in preparing presentations and communicating findings to stakeholders.

• Assist in logistics, such as equipment purchasing and hiring student assistants

Job Requirements:

• Bachelor’s or Master’s degree in computer science, data science, information studies, or related fields.

• Strong programming skills (e.g., Python, R) for data scraping, cleaning, and analysis.

• Experience with data visualization tools and libraries (e.g., Tableau, matplotlib, ggplot).

• Familiarity with social media APIs and web scraping frameworks.

• Ability to run statistical and computational analyses (e.g., regression, network analysis, machine learning).

• Academic writing skills and willingness to co-author publications.

• Strong problem-solving skills and ability to work independently and collaboratively.

• Prior experience in computational social science or related projects is preferred; Entry level candidates with relevant skills are welcome to apply.

We regret to inform that only shortlisted candidates will be notified.

Job Summary

We are a project team comprising researchers from SingHealth, A*STAR, and Synapxe, developing a product to enhance the management and treatment of patients with lower respiratory tract infections (LRTIs)—a condition that accounts for nearly 489 million cases globally and is a major contributor to inappropriate antibiotic use in Singapore. 

Over 40% of prescriptions for LRTIs are unnecessary, contributing to antimicrobial resistance (AMR)—a top global health threat. Traditional stewardship programs are expert-dependent and resource-intensive, limiting scalability.

To overcome these challenges, we are building an AI-powered Large Language Model platform that uses routinely collected clinical data to help clinicians identify cases that do not require antibiotics, thereby reducing unnecessary prescriptions.

Role:

As a Research Officer, you will play a central role by integrating Large Language Models (LLMs) for intelligent processing of unstructured clinical text—such as physician notes, discharge summaries, and radiology reports. Your work will help build explainable, scalable, and real-time AI recommendations that assist clinicians at the point of care for lower respiratory tract infections.

Key Responsibilities:

• Contribute to the LLM development, focusing on enhancing unstructured data processing for clinical and biomedical applications.
• Fine-tune and train LLMs (e.g., LLaMA, Mistral, Phi, and the GPT family) using supervised and instruction-based datasets.
• Design and implement pipelines for data cleaning, preprocessing, and tokenisation of large-scale text corpora.
• Integrate retrieval-augmented generation (RAG) and knowledge graph components for domain adaptation.
• Evaluate model performance using BLEU, ROUGE, BERTScore, and factual consistency metrics.
• Develop optimised PEFT/LoRA/QLoRA fine-tuning frameworks for efficiency on GPU clusters.
• Collaborate with researchers to design experiments, interpret results, and publish findings.
• Maintain reproducible codebases, documentation, and experiment logs.

Requirements:

• Master or Bachelor in computer science, data science, artificial intelligence, computational linguistics, or related disciplines.
• Strong experience in Natural Language Processing (NLP), transformer-based models, and text generation.
• Proficiency in Python, PyTorch, Hugging Face, Transformers, and LLM fine-tuning libraries (e.g., PEFT, DeepSpeed, bitsandbytes).
• Experience with text and data processing, including annotation, tokenisation, and augmentation.
• Familiarity with vector databases (FAISS, Qdrant) and RAG pipelines.
• Understanding of GPU-based training, distributed model optimisation, and experiment tracking (e.g., MLflow, W&B).
• Strong analytical, communication, and collaborative skills.

Preferred Experience

• Research experience with open-weight LLMs or domain-specific adaptation (e.g., biomedical).
• Experience with multi-agent frameworks, prompt engineering, or LLM safety evaluation.
• Familiarity with cloud computing (AWS, Azure) or on-prem GPU clusters.

What We Offer

• Opportunity to work on cutting-edge LLM research with measurable real-world impact.
• Access to high-performance GPU infrastructure and interdisciplinary collaborations.
• Mentorship and opportunities for publication, conference presentation, and project leadership.

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Fellow-%28Maritime-Safety-and-Data-Analytics-%29/32512-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

The project will be engaging a Research Fellow to undertake advanced research on AIS-driven multi-vessel interaction analysis, with a particular focus on estimating navigational capacity in restricted and congested waterways (e.g., port approaches, straits, and narrow channels). Leveraging large-scale Automatic Identification System (AIS) datasets, the study will systematically extract vessel trajectories, characterize interaction patterns among heterogeneous vessel types, and identify critical factors influencing traffic flow efficiency and safety. It will further integrate multi-source data, including high-resolution meteorological and oceanographic information (e.g., wind, waves, currents, and visibility), to capture the impact of environmental conditions on vessel behavior and waterway capacity. Advanced data processing pipelines will be developed to handle massive, noisy, and heterogeneous datasets, ensuring data quality, scalability, and computational efficiency.

The selected RF will be required to collaborate closely with project team members on a range of activities:
(1) Large AIS data process
(2) AIS data based maritime safety assessment 
(3) Capacity estimation for the restricted waters
(4) Preparation of technical reports and slides
(5) Engagement with stakeholders through meetings and discussions.

Job Requirements

• Possess a PhD in Maritime Safety, Computer Engineering/Science, Data Science related subjects (incl. PhD to be received in 6 months after starting work).
• Experience in developing AIS data-driven models.
• Ability to sophisticatedly use at least one programming language, e.g., Python, R Java, SQL, Julia, C# and C++.
• Excellent interpersonal and communication skills.
• Values teamwork and display great leadership. Able to self-manage and self- motivated, with minimum supervision. 
• Open to fixed-term contract.
      
Prioritized Requirements:
• AIS Data mining and analysis, machine learning skills.
• Research experience on maritime risk assessment and big AIS data processing.
• Good publication record in international leading journals.

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Fellow-%28Traffic-safety-and-data-analytics%29/32494-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

A few Research Fellows (RFs) will be engaged in a research project to develop a robust, data-driven, and AI-powered framework for evaluating the impact of vehicle speed management policies, with a particular focus on age-related vulnerability. The proposed approach will explicitly account for the age profiles of road users, multiple vehicle types (e.g., passenger cars, trucks, and buses), as well as roadway and environmental conditions.The project aims not only to deepen the understanding of how age and vehicle speed jointly influence crash frequency and injury severity, but also to deliver practical tools to support the design of inclusive and adaptive speed management policies for an ageing population. In addition to utilizing road crash data, the project will also collect and incorporate driving simulator data to support model development and validation.

The selected RF will be required to collaborate closely with project team members on a range of activities:

•    Traffic safety related data collection such as large-scale driving simulator experiments
•    AI-based traffic safety modelling and analysis, 
•    preparation of technical reports and slides, 
•    engagement with stakeholders through meetings and discussions.

Job Requirements

•    Possess a PhD in Traffic Safety, Computer Engineering/Science, Data Science related subjects (incl. PhD to be received in 6 months after starting work).
•    Experience in developing driving simulator-based models.
•    Ability to sophisticatedly use at least one programming language, e.g., Python, R Java, SQL, Julia, C# and C++.
•    Excellent interpersonal and communication skills.
•    Good teamwork and leadership, self-manage and motivated, with minimum supervision. 
•    Open to fixed-term contract.
    
Prioritized Requirements:

• driving simulator-based model development, machine learning skills.
• Research experience on traffic safety and driving simulator usage.
• Good publication record in international leading journals.

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Fellow-%28Traffic-safety-and-data-analytics%29/32493-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

A few Research Fellows (RFs) will be engaged in a research project to develop a robust, data-driven, and AI-powered framework for evaluating the impact of vehicle speed management policies, with a particular focus on age-related vulnerability. The proposed approach will explicitly account for the age profiles of road users, multiple vehicle types (e.g., passenger cars, trucks, and buses), as well as roadway and environmental conditions.The project aims not only to deepen the understanding of how age and vehicle speed jointly influence crash frequency and injury severity, but also to deliver practical tools to support the design of inclusive and adaptive speed management policies for an ageing population. In addition to utilizing road crash data, the project will also collect and incorporate driving simulator data to support model development and validation.

The selected RF will be required to collaborate closely with project team members on a range of activities:

•    Traffic safety related data collection such as large-scale driving simulator experiments
•    AI-based traffic safety modelling and analysis, 
•    preparation of technical reports and slides, 
•    engagement with stakeholders through meetings and discussions.

Job Requirements

•    Possess a PhD in Traffic Safety, Computer Engineering/Science, Data Science related subjects (incl. PhD to be received in 6 months after starting work).
•    Experience in developing driving simulator-based models.
•    Ability to sophisticatedly use at least one programming language, e.g., Python, R Java, SQL, Julia, C# and C++.
•    Excellent interpersonal and communication skills.
•    Good teamwork and leadership, self-manage and motivated, with minimum supervision. 
•    Open to fixed-term contract.
      
Prioritized Requirements:

• driving simulator-based model development, machine learning skills.
• Research experience on traffic safety and driving simulator usage.
• Good publication record in international leading journals.

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Associate-%28Comparative-Effectiveness-and-Real-World-Data-Analytics%29/32515-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

Applications are invited for the following full-time position in the Saw Swee Hock School of Public Health:

Research Associate (Comparative Effectiveness and Real-World Data Analytics)

Position Summary
The Centre for Health Intervention and Policy Evaluation Research (HIPER) (https://hiper.nus.edu.sg/) at the Saw Swee Hock School of Public Health (SSHSPH), NUS, is seeking to hire a researcher with a relevant Master’s degree to lead Phase III of the RODEO project, focusing on comparative analyses using oncology real-world data. The candidate will be responsible for designing and conducting comparative effectiveness studies across multiple myeloma, non-small cell lung cancer, and ovarian cancer using structured and processed unstructured data from electronic medical records.

The role is particularly suited to candidates with training in biostatistics, epidemiology, health services research, health economics, public health, data science, or a related field, and who have experience working with observational healthcare datasets.

Key Responsibilities:
• Lead the design and execution of comparative effectiveness analyses using real-world oncology data
• Develop study protocols and statistical analysis plans for observational cohort analyses
• Apply appropriate methods to address confounding, selection bias, missing data, and other threats to internal validity in real-world studies
• Conduct analyses using methods such as propensity score matching, inverse probability weighting, direct covariate adjustment, difference-in-differences, instrumental variables, and survival analysis
• Perform time-to-event analyses, including overall survival and progression-related outcomes
• Support partitioned survival analyses and other methods relevant to economic evaluation and health technology assessment
• Work closely with clinicians, health economists, and data scientists to identify clinically relevant treatment comparisons and confounders
• Collaborate with data engineers and analysts to ensure appropriate variable construction and dataset readiness
• Interpret findings in the context of health technology assessment, reimbursement, and policy decision-making
• Prepare technical reports, presentations, manuscripts, and conference abstracts
• Ensure analyses adhere to methodological standards such as STROBE and ISPOR good practice recommendations for real-world evidence studies
• Participate in project meetings with collaborators across NUS, NCIS, NUH, TTSH, NCCS, and other partner institutions

Requirements:
• Experience working with large observational healthcare datasets, registries, or electronic medical records
• Strong knowledge of causal inference and comparative effectiveness methods
• Experience using statistical software such as R, Stata, SAS, or Python
• Familiarity with oncology data, survival analysis, and longitudinal data analysis will be an advantage
• Understanding of health technology assessment, outcomes research, or economic evaluation is desirable
• Strong written and verbal communication skills
• Ability to work independently while collaborating effectively in a multidisciplinary research team

Preferred Attributes:
• Prior experience with real-world evidence generation in oncology
• Familiarity with OMOP common data model, OHDSI tools, or TRUST platform datasets
• Experience translating statistical findings into policy-relevant insights for clinicians, hospital leaders, or HTA agencies
• Interest in contributing to publications and future grant applications

Qualifications

• Master’s degree in Biostatistics, Epidemiology, Public Health, Health Economics, Data Science, Statistics, Health Services Research, or a related discipline

Ever wanted a job that feels more like real-life experiences than routine work?

Step into the world of live events, roadshows, and brand activations, where you meet new people, create energy, and bring brands to life.

✨ What you’ll do:

• Represent brands at events & roadshows
• Engage with customers face-to-face
• Work in a fun, fast-paced environment

✨ We offer:

• No experience needed (training provided)
• Weekly pay + attractive incentives
• Career progression opportunities
• Fun, energetic, and supportive team environment