Ok so I found two websites that could help. The one I found first is more intuitive to go through, but it looks like it could be outdated as the second website I found seems more recent so it probably has a more complete genome to use as the reference. (Sorry, I haven't worked personally with sunflowers and don't know for sure where you would go for the "official" reference genome, or if there even is an "official" one, but hopefully one of these websites will help.)
1) This is a website I found that looks like it is a website for the sunflower genome and includes gene annotation information. (The recent publications list only goes up till 2015 though so it may not be the most recent data)
If you scroll down to the bottom of this page there is a text file detailing the descriptions of how the files are formatted.
For example, the linked to file gives the following:
If you download this fasta file you could then create a python script that will read in the file and extract the headers. If each header is a string variable called "mytext", you could then extract the TranscriptID, GeneID, and chromosomal info and using mytext.split(" "). You could then split on "_" to further parse the chromosomal location info. You could save the extracted information in a dictionary using the gene name as a key and parse through the original file you mentioned containing the list of genes, in order to get the chromosomal location information you desire. The one caveat is that this is assuming that your gene names are the same as the geneIDs the above website uses.
I did not exhaustively examine the list of files and perhaps there is another one that would be a better one to parse through for your purpose, but the linked to text file gives more details on the files and their formats and presumably they could be parsed in a similar manner.
2) Double checking my first answer I found this article:
Badouin H, Gouzy J, Grassa CJ, Murat F, Staton SE, Cottret L,
Lelandais-Brière C, Owens GL, Carrère S, Mayjonade B, Legrand L. The
sunflower genome provides insights into oil metabolism, flowering and
Asterid evolution. Nature. 2017 Jun;546(7656):148.
Which links to a website as follows:
"The integrative web interface Heliagene provides visualization, querying tools for data mining and network exploration for the community (https://www.heliagene.org)."
So it looks like this website also has reference genomes for sunflowers, although a lot of the annotation information seems to be locked unless you have a login. Like I mentioned previously, this website is a little less intuitive to go through but you might be able to find something helpful here if you think the other website isn't official or recent enough.