The intern will contribute to multiple activities focused on Graeco-Roman bioarchaeology. In particular, he/she will help update Bi(bli)oArch (https://www.biblioarch.com/), an open access bibliographic database for human bioarchaeological studies in the region, as well as help develop a similar database for zooarchaeological studies (ZooBi(bli)oArch). In addition, the intern will join our team in the compilation of published bioarchaeological data and their organization in a new database (Graeco-Roman Bioarch). Finally, the intern will gain hands-on experience in human osteoarchaeology while contributing to the study of human skeletal remains currently analyzed at our facilities, as well as in digitization methods while assisting in the 3D documentation of selected remains, which will then form part of our in-house reference collections. 

1) Digitisation (3D) of as many comparative specimens as possible

2) Curation of comparative reference collection (adding new specimens, marking and documenting them)

1) Digital (3D) replicas of most commonly used specimens (sheep, goat, pig, dog)

2) Expansion of comparative reference collection

Training in the field in the recovery of organic remains

Training in the laboratory in archaeobotany

Acquire of knowledge of field methods (not known to most) and basic training in archaeobotanical research (sorting of samples and basic identification)

In this Internship, a multiscale model fully representing mechanical deformation will be developed for the identification of geometrical and mechanical properties of the interfacial layer in polymer nanocomposite (PNC) with nanoparticle (NP) filler. The mechanical properties of PNC will be derived using atomistic molecular dynamics (MD) simulations. Through the analysis of strain energy and its distribution inside the nanocomposite unit cell, the polymer layer at the vicinity of the NP surface (interphase) will be revealed. Taking account of this unusual reinforcing effect using a finite element (FE) model or homogenization approach, we will implicitly solve for the size and mechanical properties of the effective interphase, which has an equivalent strain energy around the NP, as well as the global elastic stiffness of the nanocomposite that is equivalent to the corresponding value from MD simulations. The equivalent continuum model thus properly predicts the local stress distribution at the adsorbed polymer-NP interface as well as the overall mechanical properties of nanocomposite. For the latter a methodology involving machine learning (ML) algorithms could be used.  

The overarching goal is to compute the rigidity of the interphase surrounding the nanoparticles by combining atomistic simulations, continuum (finite element) models and data-driven algorithms. 

We are looking for an intern who will apply Artificial Intelligence and Machine Learning techniques to big-data problems.

Under the supervision and guidance of their mentor, as well as the cooperation of other research engineers, they will learn new and invaluable skills. By using predictive models, experimental design methods, and optimization techniques, the intern will be working on the research and development of various projects.

We expect for interns to be committed to their internship and to be keen to learn as much as they can from their mentors.

For the interns to learn key skills, which they can use in their future academic studies, or later in their careers. 

We are looking for an intern who will apply Data Science techniques to big-data problems.

Under the supervision and guidance of their mentor, as well as the cooperation of other research engineers, they will learn new and invaluable skills to be able to implement:

• Automation: Developing innovative solutions for automating data handling and analytics processes using Python or similar programming languages
• Data engineering: Exploring efficiencies in data flow from various sources of data to analysis ready data sets
• Data analytics: Developing code for processing or exploration of data, potentially aggregating with other datasets, in order to uncover insights from the data

We expect for interns to be committed to their internship and to be keen to learn as much as they can from their mentors.

For the interns to learn key skills, which they can use in their future academic studies, or later in their careers.

We are looking for an intern who will apply system administration skills towards the operation of a High Performance Computing Infrastructure.

Under the supervision and guidance of their mentor, as well as the cooperation of other system engineers, they will work together to support and maintain the infrastructure. The intern will learn key skills to achieve this, including:

• Installing, configuring and maintaining HPC hardware and software
• Monitoring system performance and identifying potential issues
• Providing technical support to HPC users – across various levels of difficulty

We expect for interns to be committed to their internship and to be keen to learn as much as they can from their mentors.

For the interns to learn key skills, which they can use in their future academic studies, or later in their careers.

We are looking for interns with a challenging computational project to be run on High Performance Computing Systems.
Under the supervision and guidance of their mentor, as well as the cooperation of other researchers, they will work on developing and optimising their code, so they can run faster and more efficiently on a supercomputing system. To achieve this, the intern will learn key skills, including:

• Designing and implementing parallel algorithms or using software libraries for scientific simulations
• Benchmarking and profiling code performance to identify bottlenecks and parts of code which can be made more efficient
• Code debugging and reviewing to ensure software is of a required quality and performance.
• Application of advanced numerical techniques, such as machine learning and state-of-the-art algorithms

The interested applicants should provide a brief description of their research project from a scientific and computational point of view.

We expect for interns to be committed to their internship and to be keen to learn as much as they can from their mentors.

June 1

• Industrial engineering
• Mechanical engineering
• Health and Safety
• Environmental engineering
• Materials engineering

The student should be familiar with basic computer software (e.g., Word, excel or similar).

The applicant must be a university student in one of the following specialties:

• Industrial engineering
• Mechanical engineering
• Health and Safety
• Environmental engineering

The student should be familiar with basic computer software (e.g., Word, excel or similar).