3. Project Title: Evaluation of heat tolerant cows carrying the slick genetic variant
4. Project Title: Development and application of an OMIA-based variant screening tool
5. Project Title: Embryo Programme Analysis: Efficiency, Metrics, and Strategic Benefit
6. Project Title: Impact of Genetic Diversity in LIC’s Breeding Schemes
8. Project Title: Automated Data Product Provisioning with Built-in Data Quality & Monitoring
1. Project Title: Comparative Genomic Analysis of Staphylococcus epidermidis from New Zealand Bovine Mastitis Isolates and Overseas Isolates
Briefly overview the scope of the R&D project the student will be contributing to.
The student will be focused on the coagulase negative staphylococcal bacterial species Staphylococcus epidermidis which has been identified as a causative agent of bovine mastitis. Little is known about bovine S. epidermidis in New Zealand, therefore the students’ main purpose to use a dataset of bovine S. epidermidis isolates to characterise the genomic profiles of 200 S. epidermidis isolates, perform comparisons to overseas bovine, caprine, ovine and human isolates to distinguish if New Zealand has a distinct pathogen population.
The student will be jointly supervised by LIC and Cognosco with the student based at LIC, Riverlea Road, Hamilton under supervision of Emma Voss (Postdoctoral Scientist in Animal Health), however animal health/veterinary insights will be provided by Ali Karkaba and Scott McDougall based at Cognosco. This project offers a unique opportunity for a student to contribute to foundational research on non-aureus Staphylococci (NAS) an urgent and underexplored area in mastitis research.
Briefly demonstrate how the student will contribute to the project
The student will process raw genomic data, perform annotation, investigate antimicrobial resistance genes with phenotypic testing and phylogenetic analysis. These analyses will contribute to a more thorough understanding of bovine S. epidermidis.
How will LIC benefit from employing the student
The company will benefit by advancing animal health R&D through enhanced genomic surveillance of mastitis pathogens and by addressing key industry challenges related to mastitis infections. This project also provides critical comparative insights into bovine Staphylococcus epidermidis—an urgent knowledge gap in mastitis research—while strengthening ongoing collaboration with veterinary researchers at Cognosco.
Key Skills Required
This project combines bioinformatics approaches and comparative genomics techniques:
• The ideal applicant will have experience with the command line and be comfortable using Bash, as many genomic comparison tools require command-line operations. Familiarity with Python and R is preferred but not essential.
• A good understanding of antimicrobial resistance mechanisms and phenotypic validation methods is desirable.
• Experience working with sequencing data, particularly Illumina short-read analysis, is an advantage
Mentor name and job title: Emma Voss Post Doctoral Scientist (Animal Health Genetics)
2. Project Title: Construction and analysis of pangenome graphs for de novo structural variant discovery.
Briefly overview the scope of the R&D project the student will be contributing to.
Spontaneous genetic alterations, or de novo mutations, are a fundamental aspect of genome biology and play a pivotal role in shaping genetic diversity. Although many of these mutations will have no direct functional effect, a small proportion can be deleterious and, in rare cases, beneficial. A vast majority of de novo mutations will disappear from populations due to genetic drift. In livestock breeding, artificial insemination, which enables the widespread use of a few elite bulls to sire the next generation, creates the potential for these types of mutations to increase in frequency within a few generations. This means that accurate detection of de novo mutations is crucial in understanding novel genetic variation in our breeding populations before carrier bulls are widely used. Structural mutations, such as large insertions, deletions, inversions, and translocations, affect more of the genome per nucleotide changes than any other class of sequence variant. However, detecting structural variants has historically been challenging using short-read sequence data. Long-read sequencing offers novel insights into the genome’s structural complexity and an opportunity for the precise detection of structural variation. The aim of this project is to use long-read sequence data from trios to create pangenome graphs and trial different methods for structural variant discovery.
Briefly demonstrate how the student will contribute to the project
The student will investigate how useful pangenome graphs might be for the discovery of de novo structural variation. They will:
- Construct pangenome graphs using long-read sequence data,
- Conduct a literature review on pangenome graphs and their use for structural variant discovery, and
- Implement some of these methods, comparing the results from each approach.
How will LIC benefit from employing the student
This project will enable us to explore the usefulness of pangenome graphs for structural variant discovery. Mining the literature to investigate pangenome analysis tools and trialling these tools on our dataset will be a time-consuming but informative activity. These insights will enable us to decide whether pangenome-based methods will be useful for screening bulls for de novo structural variants in the future.
Key Skills Required
This project is suited for a third-year or postgraduate computer science or biology student with experience in bioinformatics and high-performance computing. A basic understanding of genetics would be favourable but not required.
Mentor names and job titles:
Fenella Deans; Scientist – Molecular Genetics
Laura Duntsch; Scientist – Molecular Genetics
3. Project Title: Evaluation of heat tolerant cows carrying the slick genetic variant
Briefly overview the scope of the R&D project the student will be contributing to.
LIC is breeding cattle that carry a genetic variant (known as the SLICK variant) that reduces hair length and improves heat tolerance. We are investigating and using methods that quantify the benefits of the SLICK gene to heat tolerance in carrier cattle and the contribution of hair length to this effect.
This project will examine physiological and behavioural differences linked to the SLICK trait. In particular, this will involve data collection and analysis of rumen temperatures (from a rumen bolus), drinking behaviour, grazing and rumination times and milking performance. The student will also contribute to the detailed analysis of the SLICK hair phenotype and contrasting measurements with control cows.
LIC has a herd of ~70 SLICK lactating cows along with a similar number of matched control cows on its research farm that have been/will be used to provide the samples and datasets for analysis.
Briefly demonstrate how the student will contribute to the project
The student will play a central role in the collation and analysis of data from the milking cows. Some of the research will be desk-based using existing datasets but a significant proportion of the work will be direct involvement in data collection on farm particularly when cattle are thermally challenged. The student will be required to present their work to the LIC Research and Development team at the end of the project and will also provide a written report.
How will LIC benefit from employing the student
This project will help collate the data obtained from SLICK cattle on the research farm that are monitored using wearable and rumen bolus devices. This will be of value to the wider SLICK project through better understanding of the value of SLICK to animal welfare and performance.
Key Skill Sets
We are looking for a student that has data analysis skills at least Excel and R. Animal science/genetics knowledge would be an advantage.
Mentor names and job titles:
Gemma Worth; Research Associate • Reproduction Research
4. Project Title: Development and application of an OMIA-based variant screening tool
Briefly overview the scope of the R&D project the student will be contributing to.
This R&D project aims to develop a comprehensive, annotated variant call format (VCF) file containing all known likely causal variants for single-gene traits (both disease and non-disease) in dairy and beef cattle, as catalogued by the Online Mendelian Inheritance in Animals (OMIA) database. The project will also include screening of existing genotype and sequence data to identify the presence and frequency of these variants in New Zealand’s dairy cattle population. The resulting resource will support ongoing genetic monitoring and breeding decisions.
Briefly demonstrate how the student will contribute to the project
The student will extract and curate variant data from OMIA, focusing on entries with known likely causal variants. They will compile this into a standardized VCF file, enriched with metadata such as breed specificity, phenotypic consequences, and variant frequencies. The student will also screen internal SNP array and whole-genome sequence datasets to identify these variants in New Zealand’s dairy cattle population and generate summary statistics. A literature review may be conducted to identify additional variants not yet included in OMIA, ensuring the resource is as comprehensive as possible.
How will LIC benefit from employing the student
Employing the student will enable the company to build and maintain a high-value genomic resource that facilitates rapid screening of known high-impact variants in any new animal with available genetic data. This will enhance LIC’s ability to monitor inherited traits, support informed breeding decisions, and stay at the forefront of genetic risk management. The project also lays the groundwork for future integration of novel variant discoveries and contributes to the company’s innovation pipeline in animal genomics.
Key Skill Sets
This project is suited for a third-year or postgraduate biology student familiar with genomic data formats (e.g., VCF) and high-performance computing. The student should have strong research and critical analysis skills, including the ability to conduct a structured literature review. Familiarity with scientific databases, academic search tools (e.g., PubMed, Google Scholar), and the ability to synthesize findings from peer-reviewed sources would be advantageous.
Mentor names and job titles:
Laura Duntsch; Scientist – Molecular Genetics
Thomas Lopdell; Scientist – Molecular Genetics
5. Project Title: Embryo Programme Analysis: Efficiency, Metrics, and Strategic Benefit
Briefly overview the scope of the R&D project the student will be contributing to.
There his project evaluates LIC’s embryo transfer (ET) and IVF programmes within the bull acquisition pipeline. The student will review multi-season data to analyse success rates, embryo-to-bull conversion ratios, genetic merit outcomes, and progress of offspring purchased for SPS to then marketed. The project will identify where gains or inefficiencies exist and recommend improvements to maximise the return on LIC's embryo investments and the long-term benefits to the breeding pipeline.
Briefly demonstrate how the student will contribute to the project
The student will:
• Collate and analyse embryo programme data from LIC databases.
• Create dashboards or reports to track key performance indicators (KPI).
• Work with analysts and breeding managers to interpret results and develop insights.
• Present findings and propose strategic improvements at the end of the internship.
How will LIC benefit from employing the student
• Immediate insights into the efficiency of a high-investment breeding strategy (ET).
• Potential to improve programme outcomes and ROI through data-driven recommendations.
• Development of a student who could transition into a Genetics Intern or Analyst role post-graduation.
• Opportunity to benchmark the student’s capability for future recruitment into Genetics and Breeding roles.
Key Skills Required
This role requires a strong foundation in animal science, genetics, and dairy farming, combined with competency in data handling, analysis, and visualisation using tools such as Excel, R, Python, SQL, or Power BI. The ideal candidate will be an analytical, detail-oriented, and self-motivated communicator who can collaborate effectively and build strong relationships across technical and non-technical teams
Mentor names and job titles:
Kelli Buckley; Bull Acquisition Manager
Esther Donkersloot; Breeding Scheme Technical Manager
6. Project Title: Impact of Genetic Diversity in LIC’s Breeding Schemes
Briefly overview the scope of the R&D project the student will be contributing to.
This project explores the impact of genetic diversity and inbreeding across LIC’s core breeds: Friesian, Jersey, and SGL Dairy. Using genomic and pedigree data, the student will quantify diversity trends, model genetic gain versus inbreeding thresholds, and assess how LIC can optimise both short-term performance and long-term sustainability.
Exploring the influence of genetic diversity across the Jersey, Friesian, and SGL dairy populations. Assessment of genetic gain trade-offs, inbreeding levels, and strategies for sustainable progress.
Briefly demonstrate how the student will contribute to the project
The student will:
• Use inbreeding and diversity metrics from LIC’s database and AE tools.
• Work with analysts to run modelling scenarios simulating different inbreeding thresholds.
• Evaluate diversity trends by breed, cohort, and genetic pathway.
• Recommend acceptable inbreeding limits that balance genetic progress with future resilience.
How will LIC benefit from employing the student
• Clarity on the current genetic diversity landscape in the breeding programme.
• Evidence-based guidelines on inbreeding management for Friesian, Jersey, and SGL.
• A step toward future proofing the scheme from narrowing genetic pools.
• Development of future talent familiar with genomic selection and sustainability trade-offs.
Key Skills Required
This role requires a strong foundation in animal science, genetics, and dairy farming, combined with competency in data handling, analysis, and visualisation using tools such as Excel, R, Python, SQL, or Power BI. The ideal candidate will be an analytical, detail-oriented, and self-motivated communicator who can collaborate effectively and build strong relationships across technical and non-technical teams
Mentor names and job titles:
Kelli Buckley; Bull Acquisition Manager
Esther Donkersloot; Breeding Scheme Technical Manager
7. Project Title: Improving Calving Ease in Holstein Friesians: Balancing Suitability for Yearling Matings with Breed Integrity
Briefly overview the scope of the R&D project the student will be contributing to.
This project focuses on identifying opportunities to improve calving ease in Holstein Friesian sires, particularly for safe use over yearling heifers, without diluting the genetic strengths that define the breed. The student will assess current calving ease data, breed proportion trends, and explore strategies for generating calving-ease-friendly sires that still meet the performance, component, and size expectations of a high-value Friesian bull.
Briefly demonstrate how the student will contribute to the project
The student will:
• Analyse calving ease trends in current and historical Holstein Friesian bulls used over yearlings.
• Assess relationships between calving ease, breed purity (proportion), liveweight, and production traits.
• Investigate international strategies or genetics that improve calving ease in large-frame breeds.
• Propose selection or breeding to enhance yearling mating options without compromising core breed attributes.
How will LIC benefit from employing the student
• Deeper understanding of current gaps in Friesian calving ease and how this impacts yearling matings.
• Identification of potential bull types or pathways to develop safer mating options.
• Insights that could support the development of a calving-ease sub-index or breeding goal within Friesian selection.
• Strengthened future talent pipeline in the space of breed-specific strategy and structural trait analysis.
Key Skills Required
This role requires a strong foundation in animal science, genetics, and dairy farming, combined with competency in data handling, analysis, and visualisation using tools such as Excel, R, Python, SQL, or Power BI. The ideal candidate will be an analytical, detail-oriented, and self-motivated communicator who can collaborate effectively and build strong relationships across technical and non-technical teams
Mentor names and job titles:
Kelli Buckley; Bull Acquisition Manager
Esther Donkersloot; Breeding Scheme Technical Manager
8. Project Title: Automated Data Product Provisioning with Built-in Data Quality & Monitoring
Briefly overview the scope of the project the student will be contributing to.
The intern will work on two key use cases
1. Build tooling to turn standard data model definitions into Terraform for automated deployment on LIC’s enterprise data platform. Every new data product will launch with embedded data quality checks and monitoring from day one.
2. Develop an AI-driven approach for monitoring platform data quality and detecting anomalies in near real time.
Briefly demonstrate how the student will contribute to the project
The student will:
• The intern will work on two key use cases
• Build tooling to turn standard data model definitions into Terraform for automated deployment on LIC’s enterprise data platform. Every new data product will launch with embedded data quality checks and monitoring from day one.
• Develop an AI-driven approach for monitoring platform data quality and detecting anomalies in near real time.
How will LIC benefit from employing the student
Faster, automated deployment of new data products, reduced manual work, stronger governance, and a reference pattern for future automation. Potential to add AI-driven monitoring to proactively detect quality issues.
Key Skills Required
IT/Computer Science students with:
• Basic scripting: Python or .NET; Terraform for automation would be a plus.
• Data basics: Schema fundamentals, data validation, and quality checks.
• Cloud exposure: Familiarity with cloud platforms.
• AI/ML project experience: Hands-on work with model development or anomaly detection from coursework or personal projects.
Mentor name and job title:
Vik Mohan – Principal Technologist Data & Analytics