For instance, recent graduates who have experienced in computational modeling and simulation based- on first-principles methods , are often asked for experience in code development, coding languages (FORTRAN, C, Python) and on the top of that Machine learning in addition to high-throughout screening. On the other hand , employers (especially universities for postdocs) are demanded experience which should be a one-to-one match with their project. That is a disadvantage for postdocs and early career researchers as they have lot of new things to learn. For example , one with modeling experience would like to get their hands dirty with code development or perhaps practice machine learning etc. However, due to current rapid progress in the field many miss the chance to learn new skills and it inadvertently effect the field. I knew some employers with large collaborations have to published their advertisement several times to find a suitable candidate. How do you as a Computational Materials Science community respond to that and what will be the solution to provide adequate training particular for researchers coming from low income countries ? Also how do the researchers have to prepare for these challenges ?