Research Scientist (Food and Nutrition Data Scientist)
Post Details
Job Title
Research Scientist (Food and Nutrition Data Scientist)
Post Number
1003758
Closing Date
14 Jan 2020
Grade
SC6
Starting Salary
£31,625 - £38,575
Hours per week
37
Expected/Ideal Start Date
06 Jan 2020
Months Duration
27

Job Description

Main Purpose of the Job

Establish advanced analytical strategies for diverse food, nutritional and biological data from existing and planned human studies, using machine-learning. The successful candidate will also manage and improve our existing non-nutrient food databases.

Central to this post will be the ability to interact with the food and nutrition research communities by maintaining good working relations with our existing network, developing new collaborations, share best practice, adopt cutting edge food and nutrition-related data tools, and be a champion of our activities.

The post holder will be expected to establish predictive analytics through machine learning and deep learning where appropriate, using existing and novel data (including food composition and intake data, food imagery, consumer behaviour and lifestyle, biological measurements from blood and wearables, and gut microbiome) that are being generated from the Food Databanks National Capability (FDNC) team and our collaborators. This work will underpin our activities in the development of the FNS-cloud, and will provide opportunities for the post holder to work closely with our data science partners across Europe around food and health data. Additionally, the post holder will manage and upgrade our bioactives database (eBASIS) to improve functionality and accessibility with opportunities to automate the process of data entry.

Key Relationships

The post holder will work closely with Dr Traka and FDNC researchers to develop and expand our capabilities in big food & nutrition data. Within QIB, they will interact with other QIB research teams with complementary interests around food and nutrition research, and will liaise with the QIB statistician and the QIB Informatics Team to share best practices and ensure our computing and cloud infrastructure needs are met. Externally, they will be interacting with the food and nutrition research communities by maintaining good working relations with our existing network, developing new collaborations, share best practice, adopt cutting edge food and nutrition-related data tools, and be a champion of our activities.

Main Activities & Responsibilities

Percentage
Provide food and nutritional data analytics (advanced statistics, decision trees, algorithms, ontologies, random forest, neural networks, etc) to link continuous and non-continuous data related to foods, biochemical measurements, and biological profiles (eg diets, blood markers, wearables, gut microbiome)
40
Undertake database management (eg eBASIS) to improve access speed, usability, functionality
25
Actively engage with food and nutrition research communities by maintaining good working relations with our existing network, developing new collaborations, and participating in conferences and networking events, which will require travelling
20
Contribute to high profile scientific publications and reports as appropriate
5
Keep up–to–date with the latest advances in machine learning and current literature to maintain high-quality analytics capability
5
As agreed with line manager, any other duties commensurate with the nature of the role
5

Person Profile

Education & Qualifications

Requirement
Importance
PhD or MSc with equivalent experience in Computer Science, Data Science, Biological Sciences or a related discipline
Essential
BSc in Computer Science, Data Science, Biological Sciences or a related discipline
Essential

Specialist Knowledge & Skills

Requirement
Importance
Programming expertise in a static and a dynamically typed language, such as C++ and Python
Essential
Familiarity with source control
Essential
Knowledge of a variety of machine learning techniques (clustering, decision tree learning, neural networks, deep learning)
Essential
Knowledge of and experience with advanced statistics preferably in R (e.g. Regression Models and Time Series)
Essential
Good knowledge and experience of Bash scripting and working with UNIX/LINUX environments
Desirable
Proven ability to analyse large data sets
Desirable
Good knowledge of SQL
Desirable

Relevant Experience

Requirement
Importance
Experience of applying machine learning algorithms for food, nutrition and/or biological data analysis
Essential
Experience working with food, nutrition and/or biological data for health-related projects.
Desirable
Experience with high performance computing
Desirable
Experience in handling gut microbiome profiles
Desirable

Interpersonal & Communication Skills

Requirement
Importance
Good interpersonal skills, with the ability to work as part of a team
Essential
Demonstrated ability to work independently to tight deadlines, using initiative and applying problem solving skills
Essential
Excellent communication skills, both written and oral, including the ability to present complex information with clarity
Essential
Ability to collaborate with internal and external stakeholders
Essential

Additional Requirements

Requirement
Importance
Attention to detail
Essential
Promotes equality and values diversity
Essential
Willingness to embrace the values of the Institute, ensuring it is a great place to work
Essential
Willingness to work outside standard working hours, undertake short placements with collaborators outside the UK, and travel as required
Essential

Who We Are

Quadram Institute Bioscience

The Quadram Institute is at the forefront of a new interface between food science, gut biology and health, developing solutions to worldwide challenges in food-related disease and human health.

We are engaged in fundamental and translational food and health research, alongside clinical studies, endoscopy and industry, working together to become a leading international hub for food and health research, combining scientific excellence and clinical expertise, delivering impacts on patient care and accelerating innovation.

The Quadram Institute is a diverse and multicultural scientific community. We thrive on our international and European links, appointing staff from across the world. We expect this to continue post Brexit. However, any candidate who would like further information on current or anticipated immigration requirements can contact the HR Team on +44 (0)1603 450888 or nbi.recruitment@nbi.ac.uk.

For more information about working at Quadram Institute Bioscience, please click here.

Department

Food Databanks

Group Details

Food Databanks National Capability (FDNC) compiles and publishes data on the composition of foods eaten in the UK, which is utilised by a wide-range of stakeholders from a variety of disciplines, and underpins research in food, nutrition and health. This includes the government’s National Diet and Nutrition Survey, which is used to inform public health policy. The data held by FDNC are used to provide the nutritional information for food labelling across the UK but they also underpin research at the Quadram Institute, across Europe, and beyond into the links between diet and health, whilst also helping to inform policy to promote a healthy lifestyle.

The Nutrient Composition of UK foods (CoFID) database, held at FDNC, describes foods eaten in the UK in terms of their macronutrients, e.g. fats, protein, carbohydrates as well as their micronutrient content, which includes vitamins and minerals. The data are published in book form as McCance and Widdowson’s The Composition of Foods, as well as online.

FDNC also develops and maintains eBASIS, a database of non-nutrient plant-derived bioactive compounds with putative health benefits in foods. eBASIS provides information from published literature about bioactives with putative health benefits, or detrimental effects in humans, supported by peer-reviewed evidence.

The FDNC team has been involved and led a succession of UK- and EU-funded projects and consortia around diet and health. We are currently leading an EIT Food activity around personalised nutrition services and are a lead partner in a consortium to develop an EU-wide Food, Nutrition Security (FNS) data cloud to connect various types of data relevant for the food and health research community (2019-2023). We also work closely with QIB research groups and our Norwich Research Park (NRP) partners–the University of East Anglia and the Norfolk and Norwich University Hospital.

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We are looking for a highly motivated individual to drive food data analytics within the Food Databanks National Capability at the Quadram Institute (QI), with an interest in embracing cutting edge computational tools in the study of food composition, food intake and wider nutritional research. 

About Food Databanks National Capability:

Food Databanks National Capability (FDNC) compiles and publishes data on the composition of foods eaten in the UK, which is utilised by a wide range of stakeholders from a variety of disciplines, and underpins research in food, nutrition and health. This includes the government’s National Diet and Nutrition Survey, which is used to inform public health policy. We are additionally responsible for developing and maintaining eBASIS, a database of non-nutrient plant-derived bioactive compounds with putative health benefits in foods. eBASIS provides information from published literature about bioactives with putative health benefits, or detrimental effects in humans, supported by peer-reviewed evidence.

The FDNC team is active in participating and leading a succession of UK- and EU-funded projects and consortia around diet and health. We are currently leading an EIT Food activity around personalised nutrition services and are one of the lead partners in a consortium to develop an EU-wide Food, Nutrition Security (FNS) Data Cloud (FNS-Cloud, 2019-2023) to connect various types of data relevant for the food and health research community. We also work closely with QI research groups and our Norwich Research Park partners – the University of East Anglia and the Norfolk and Norwich University Hospital – that share an active interest in nutrition, and are involved in human studies to understand the interplay between the gut microbiome and nutrition.

The role:

This exciting role will involve two main activities:

1. Establishing predictive analytics through machine learning and deep learning, using existing and novel data (including food composition and intake data, food imagery, consumer behaviour and lifestyle, biological measurements from blood and wearables, and gut microbiome) that are being generated from the FDNC team and our collaborators. This work will underpin our activities as a lead partner in the EU-wide Food, Nutrition Security (FNS) data cloud to connect various types of data relevant for the food and health research community, and importantly will provide opportunities for the post-holder to work closely with our data science partners across Europe around food and health data.
2. Managing and upgrading our bioactives database (eBASIS) to improve functionality and accessibility, and will explore opportunities to automate the process of data entry (e.g. text mining).

Central to this role will be the ability to interact with the food and nutrition research communities by maintaining good working relations with our existing network, developing new collaborations, share best practice, adopt cutting edge food and nutrition-related data tools, and be a champion of our activities.

The ideal candidate:

This post will be well-suited to a graduate in Data Science/Computational Science with a strong interest in nutrition, or a Food Science/Biological Science graduate with a strong interest and experience in computational biology. You will have a BSc in a relevant area, and a PhD or MSc with equivalent relevant experience.

You will have strong communication skils to engage in collaborative activities in the food and health research area and interact with the nutrition research communities we are involved in. Having experience in working independently is also an advantage.

You will have prior experience preferably in Python/C++ programming and excellent knowledge of a variety of machine learning techniques (e.g. clustering, decision tree learning, neural networks, deep learning).

Strong skills in advanced statistics are essential, as are demonstrable examples of applying machine learning algorithms for data analysis, preferably but not exclusively in areas related to food and nutrition.


Additional information:

Salary on appointment will be within the range £31,625 to £38,575 per annum depending on qualifications and experience.  This is a fulltime contract for a period of 27 months with a potential to extend, depending on future funding.