12 Market Research jobs in Leeds
Lead Bioinformatician - Genomics Data Analysis
Posted 18 days ago
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Responsibilities:
- Lead the design, development, implementation, and maintenance of scalable bioinformatics pipelines for next-generation sequencing (NGS) data analysis (e.g., WGS, WES, RNA-Seq).
- Analyse and interpret complex genomic and transcriptomic datasets to identify genetic variants, gene expression patterns, and other biologically relevant insights.
- Collaborate closely with biologists, geneticists, clinicians, and computational scientists to define project goals and analytical strategies.
- Develop and apply statistical and machine learning methods for genomic data analysis.
- Ensure the quality, reproducibility, and documentation of all bioinformatics analyses and workflows.
- Stay abreast of the latest advancements in bioinformatics, genomics, and related computational fields.
- Mentor and guide junior bioinformaticians and data analysts.
- Contribute to research proposals, grant applications, and publications.
- Manage data storage, processing, and computational resources effectively.
- Present findings to scientific teams and external collaborators.
- PhD in Bioinformatics, Computational Biology, Genetics, Computer Science, or a related quantitative field.
- Minimum of 6 years of post-doctoral or industry experience in bioinformatics, with a strong focus on genomics data analysis.
- Extensive experience with NGS data processing and analysis pipelines (e.g., BWA, GATK, STAR, Salmon).
- Proficiency in programming languages such as Python, R, and shell scripting.
- Strong knowledge of statistical methods and machine learning techniques applicable to genomics.
- Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and containerization (e.g., Docker, Singularity) is highly desirable.
- Proven ability to manage complex projects and lead a small team.
- Excellent problem-solving, analytical, and critical thinking skills.
- Strong communication and presentation skills, with a track record of publications.
- Ability to work independently and collaboratively in a remote research environment.
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Senior Agronomist (Remote Sensing & Data Analysis)
Posted 4 days ago
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Key Responsibilities:
- Analyze and interpret high-resolution satellite imagery, aerial photography, and drone data to assess crop health, identify potential issues (e.g., disease, pest infestations, nutrient deficiencies), and monitor growth stages.
- Develop and refine algorithms and models for deriving key agronomic insights from remote sensing data, including vegetation indices, soil moisture content, and yield predictions.
- Utilize GIS software and data visualization tools to create clear, actionable maps and reports for agronomists and farmers.
- Provide expert agronomic advice based on data analysis, recommending tailored solutions for irrigation, fertilization, pest control, and crop rotation.
- Collaborate with R&D teams to integrate remote sensing data with other data sources (e.g., weather, soil, IoT sensor data) for comprehensive farm management insights.
- Stay abreast of the latest advancements in remote sensing technologies, agricultural drones, machine learning, and precision agriculture techniques.
- Contribute to the development and validation of new data products and services.
- Communicate technical findings effectively to both technical and non-technical audiences in a remote setting.
- Maintain accurate records and documentation of data analysis processes and findings.
- Provide training and support to internal teams and external stakeholders on the use of remote sensing tools and data interpretation.
- M.S. or Ph.D. in Agronomy, Crop Science, Soil Science, Geosciences, or a related agricultural field with a strong emphasis on data analysis and remote sensing.
- Minimum of 5 years of experience in applying remote sensing and GIS technologies to agricultural challenges.
- Proven expertise in analyzing satellite/aerial imagery (e.g., Sentinel, Landsat, Planet) and processing drone data.
- Proficiency with GIS software (e.g., ArcGIS, QGIS) and remote sensing platforms (e.g., ERDAS Imagine, ENVI).
- Strong understanding of agronomic principles, crop physiology, and soil science.
- Experience with data analysis and statistical modeling, preferably using Python or R.
- Excellent problem-solving skills and ability to work independently in a remote environment.
- Strong written and verbal communication skills, with the ability to convey complex information clearly.
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